WorldWideScience

Sample records for statins data-mining study

  1. The study on privacy preserving data mining for information security

    Science.gov (United States)

    Li, Xiaohui

    2012-04-01

    Privacy preserving data mining have a rapid development in a short year. But it still faces many challenges in the future. Firstly, the level of privacy has different definitions in different filed. Therefore, the measure of privacy preserving data mining technology protecting private information is not the same. So, it's an urgent issue to present a unified privacy definition and measure. Secondly, the most of research in privacy preserving data mining is presently confined to the theory study.

  2. LER Data Mining Pilot Study Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Young, Jonathan; Zentner, Michael D.; McQuerry, Dennis L.

    2004-10-15

    LERs consist of a one page standard form with a standard header and free text data, followed by additional continuation pages of free text data. Currently this LER data is analyzed by first inputting the heading and text data manually into a categorical relational database. The data is then evaluated by enumeration of data in various categories and supplemented by review of individual LERs. This is labor intensive and makes it difficult to relate specific descriptive text to enumerated results. State of the art data mining and visualization technology exists that can eliminate the need for manual categorization, maintain the text relationships within each report, produce the same enumerated results currently available, and provide a tool to support potentially useful additional analysis of the informational content of LERs in a more timely and cost effective manner.

  3. Statin-associated muscular and renal adverse events: data mining of the public version of the FDA adverse event reporting system.

    Directory of Open Access Journals (Sweden)

    Toshiyuki Sakaeda

    Full Text Available OBJECTIVE: Adverse event reports (AERs submitted to the US Food and Drug Administration (FDA were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA reductase inhibitors (statins and to attempt to determine the rank-order of the association. METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. CONCLUSIONS: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events.

  4. Using data mining techniques to characterize participation in observational studies.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

  5. A Study of Typhoon Intensity Change by Data Mining Technique

    Science.gov (United States)

    Ho, C.-R.; Cheng, Y.-H.; Lin, C.-Y.; Kuo, N.-J.; Huang, S.-J.

    2012-04-01

    The western North Pacific is the area of the most frequent typhoons strikes over the world. Each year, about 6-10 typhoons of Category 4 or 5 in the Saffir-Simpson hurricane scale emerging in the western North Pacific. These severe typhoons not only bring drastic impact for the coastal area through powerful winds and torrential rain, but also stir the ocean surface and cause upper ocean response along its passage. The ocean response plays one of the most important roles in air-sea interaction. The primary purpose of this study is employing a data mining technique in retrieving passible influence parameters on typhoon intensity change. The possible influence parameters include sea surface temperature, atmospheric water vapour, rain rate, sea surface height anomaly, and air-sea temperature difference. The sea surface temperature data is derived from the Microwave Imager (TMI) and the Advanced Microwave Scanning Radiometer. The atmospheric water vapour and rain rate data are from TMI. The sea surface height anomaly is a blended data accessed from satellite altimetry, and the air temperature data is from National Centre for Environmental Prediction. Totally 14 Category-5 typhoons occurred between 2003 and 2007 in the western North Pacific are analyzed in this study, which decision tree algorithm is applied as the data mining technique. The results show that air-sea temperature difference and sea surface temperature intensify the typhoon most. Due to higher sea surface temperature can provide more heat potential to the atmosphere, and the larger temperature difference between sea and air can also provide more heat energy to the atmosphere, once a typhoon passes over the ocean where sea surface temperature is higher than air temperature, about 88% of typhoon intensity is enhanced. This data mining model is further validated by using the data of super typhoon JANGMI (2008). It shows 82.3% of accuracy prediction and 85.7% for precision.

  6. Data Mining and Complex Problems: Case Study in Composite Materials

    Science.gov (United States)

    Rabelo, Luis; Marin, Mario

    2009-01-01

    Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

  7. Data Mining Applications: A comparative Study for Predicting Student's performance

    OpenAIRE

    Yadav, Surjeet Kumar; Bharadwaj, Brijesh; Pal, Saurabh

    2012-01-01

    Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used su...

  8. A Case Study for Student Performance Analysis based on Educational Data Mining (EDM)

    OpenAIRE

    Daxa Kundariya; Prof. Vaseem Ghada

    2016-01-01

    Educational Data Mining (EDM) is a study methodology and an application of data mining techniques related to student’s data from academic database. Like other domain, educational domain also produce vast amount of studying data. To enhance the quality of education system student performance analysis plays an important role for decision support. This paper elaborates a study on various Educational data mining technique and how they could be used to educational system to analysis student perfor...

  9. Data mining in the study of nuclear fuel cells

    International Nuclear Information System (INIS)

    Medina P, J. A.; Ortiz S, J. J.; Castillo, A.; Montes T, J. L.; Perusquia, R.

    2015-09-01

    In this paper is presented a study of data mining application in the analysis of fuel cells and their performance within a nuclear boiling water reactor. A decision tree was used to fulfill questions of the type If (condition) and Then (conclusion) to classify if the fuel cells will have good performance. The performance is measured by compliance or not of the cold shutdown margin, the rate of linear heat generation and the average heat generation in a plane of the reactor. It is assumed that the fuel cells are simulated in the reactor under a fuel reload and rod control patterns pre designed. 18125 fuel cells were simulated according to a steady-state calculation. The decision tree works on a target variable which is one of the three mentioned before. To analyze this objective, the decision tree works with a set of attribute variables. In this case, the attributes are characteristics of the cell as number of gadolinium rods, rods number with certain uranium enrichment mixed with a concentration of gadolinium, etc. The found model was able to predict the execution or not of the shutdown margin with a precision of around 95%. However, the other two variables showed lower percentages due to few learning cases of the model in which these variables were or were not achieved. Even with this inconvenience, the model is quite reliable and can be used in way coupled in optimization systems of fuel cells. (Author)

  10. Data warehousing and data mining: A case study

    Directory of Open Access Journals (Sweden)

    Suknović Milija

    2005-01-01

    Full Text Available This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS, University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.

  11. Data Mining in Course Management Systems: Moodle Case Study and Tutorial

    Science.gov (United States)

    Romero, Cristobal; Ventura, Sebastian; Garcia, Enrique

    2008-01-01

    Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both…

  12. PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY

    Data.gov (United States)

    National Aeronautics and Space Administration — PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE*, KIRK BORNE, SANDIPAN DEY*, SUGANDHA ARORA, AND HILLOL KARGUPTA...

  13. Data mining in radiology

    International Nuclear Information System (INIS)

    Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

    2014-01-01

    Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining

  14. Data mining.

    Science.gov (United States)

    Cupples, L Adrienne; Bailey, Julia; Cartier, Kevin C; Falk, Catherine T; Liu, Kuang-Yu; Ye, Yuanqing; Yu, Robert; Zhang, Heping; Zhao, Hongyu

    2005-01-01

    Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

  15. Studies of MHD stability using data mining technique in helical plasmas

    International Nuclear Information System (INIS)

    Yamamoto, Satoshi; Pretty, David; Blackwell, Boyd

    2010-01-01

    Data mining techniques, which automatically extract useful knowledge from large datasets, are applied to multichannel magnetic probe signals of several helical plasmas in order to identify and classify MHD instabilities in helical plasmas. This method is useful to find new MHD instabilities as well as previously identified ones. Moreover, registering the results obtained from data mining in a database allows us to investigate the characteristics of MHD instabilities with parameter studies. We introduce the data mining technique consisted of pre-processing, clustering and visualizations using results from helical plasmas in H-1 and Heliotron J. We were successfully able to classify the MHD instabilities using the criterion of phase differences of each magnetic probe and identify them as energetic-ion-driven MHD instabilities using parameter study in Heliotron J plasmas. (author)

  16. A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques

    Science.gov (United States)

    Uswatun Khasanah, Annisa; Harwati

    2017-06-01

    Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

  17. A Study of Children's Musical Preference: A Data Mining Approach

    Science.gov (United States)

    Yim, Hoi Yin Bonnie; Boo, Yee Ling; Ebbeck, Marjory

    2014-01-01

    Musical preference has long been a research interest in the field of music education, and studies consistently confirm the importance of musical preference in one's musical learning experiences. However, only a limited number of studies have been focussed on the field of early childhood education (e.g., Hargreaves, North, & Tarrant, 2006;…

  18. Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

    Science.gov (United States)

    Chen, Xin

    2012-01-01

    Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

  19. Data mining

    CERN Document Server

    Gorunescu, Florin

    2011-01-01

    The knowledge discovery process is as old as Homo sapiens. Until some time ago, this process was solely based on the 'natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since 'knowledge is power'. The goal of this book is to provide, in a friendly way

  20. [Broader indication for treatment with statins; the 'heart protection study'

    NARCIS (Netherlands)

    Stalenhoef, A.F.H.; Stuyt, P.M.J.

    2002-01-01

    The introduction of statins has been a breakthrough in the treatment of hypercholesterolaemia. Statins are safe and effective in reducing the risk of coronary heart disease in the general population. The 'Heart protection study' has provided evidence for the benefit of statin treatment in much

  1. Data mining

    Energy Technology Data Exchange (ETDEWEB)

    Lee, K.; Kargupta, H.; Stafford, B.G.; Buescher, K.L.; Ravindran, B.

    1998-12-31

    This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The objective of this project was to develop and implement data mining technology suited to the analysis of large collections of unstructured data. This has taken the form of a software tool, PADMA (Parallel Data Mining Agents), which incorporates parallel data accessing, parallel scalable hierarchical clustering algorithms, and a web-based user interface for submitting Structured Query Language (SQL) queries and interactive data visualization. The authors have demonstrated the viability and scalability of PADMA by applying it to an unstructured text database of 25,000 documents running on an IBM SP2 at Argonne National Laboratory. The utility of PADMA for discovering patterns in data has also been demonstrated by applying it to laboratory test data for Hepatitis C patients and autopsy reports in collaboration with the University of New Mexico School of Medicine.

  2. A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

    Directory of Open Access Journals (Sweden)

    Zeynep CEYLAN

    2017-09-01

    Full Text Available Medication errors are common, fatal, costly but preventable. Location of drugs on the shelves and wrong drug names in prescriptions can cause errors during dispensing process. Therefore, a good drug-shelf arrangement system in pharmacies is crucial for preventing medication errors, increasing patient’s safety, evaluating pharmacy performance, and improving patient outcomes. The main purpose of this study to suggest a new drug-shelf arrangement for the pharmacy to prevent wrong drug selection from shelves by the pharmacist. The study proposes an integrated structure with three-stage data mining method using patient prescription records in database. In the first stage, drugs on prescriptions were clustered depending on the Anatomical Therapeutic Chemical (ATC classification system to determine associations of drug utilizations. In the second stage association rule mining (ARM, well-known data mining technique, was applied to obtain frequent association rules between drugs which tend to be purchased together. In the third stage, the generated rules from ARM were used in multidimensional scaling (MDS analysis to create a map displaying the relative location of drug groups on pharmacy shelves. The results of study showed that data mining is a valuable and very efficient tool which provides a basis for potential future investigation to enhance patient safety.

  3. How to Apply Data Mining Technology to the Study of Agricultural Information Data Resources?

    OpenAIRE

    Wang, Xindong; Xu, Haoyue; Gao, Qian; Cai, Haiyan; Lu, Junhai; Li, Min

    2013-01-01

    This paper makes a brief description of the definition and methods of data mining. It describes the characteristics of agricultural data (value delivery, specialization, spatio-temporal bidimensionality) and the status of application of data mining technology in agriculture.

  4. Applied data mining

    CERN Document Server

    Xu, Guandong

    2013-01-01

    Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.

  5. Statin Effects on Aggression: Results from the UCSD Statin Study, a Randomized Control Trial

    Science.gov (United States)

    Golomb, Beatrice A.; Dimsdale, Joel E.; Koslik, Hayley J.; Evans, Marcella A.; Lu, Xun; Rossi, Steven; Mills, Paul J.; Criqui, Michael H.

    2015-01-01

    Background Low/ered cholesterol is linked to aggression in some study designs. Cases/series have reported reproducible aggression increases on statins, but statins also bear mechanisms that could reduce aggression. Usual statin effects on aggression have not been characterized. Methods 1016 adults (692 men, 324 postmenopausal women) underwent double-blind sex-stratified randomization to placebo, simvastatin 20mg, or pravastatin 40mg (6 months). The Overt-Aggression-Scale-Modified–Aggression-Subscale (OASMa) assessed behavioral aggression. A significant sex-statin interaction was deemed to dictate sex-stratified analysis. Exploratory analyses assessed the influence of baseline-aggression, testosterone-change (men), sleep and age. Results The sex-statin interaction was significant (P=0.008). In men, statins tended to decrease aggression, significantly so on pravastatin: difference=-1.0(SE=0.49)P=0.038. Three marked outliers (OASMa-change ≥40 points) offset otherwise strong significance-vs-placebo: statins:-1.3(SE=0.38)P=0.0007; simvastatin:-1.4(SE=0.43)P=0.0011; pravastatin:-1.2(SE=0.45)P=0.0083. Age≤40 predicted greater aggression-decline on statins: difference=-1.4(SE=0.64)P=0.026. Aggression-protection was emphasized in those with low baseline aggression: ageaggression (N=40) statin-difference-vs-placebo=-2.4(SE=0.71)P=0.0016. Statins (especially simvastatin) lowered testosterone, and increased sleep problems. Testosterone-drop on statins predicted aggression-decline: β=0.64(SE=0.30)P=0.034, particularly on simvastatin: β=1.29(SE=0.49)P=0.009. Sleep-worsening on statins significantly predicted aggression-increase: β=2.2(SE=0.55)Paggression-increase on statins became significant with exclusion of one younger, surgically-menopausal woman (N=310) β=0.70(SE=0.34)P=0.039. The increase was significant, without exclusions, for women of more typical postmenopausal age (≥45): (N=304) β=0.68(SE=0.34)P=0.048 – retaining significance with modified age

  6. The application of data mining methods

    OpenAIRE

    Geng, Xiaoli

    2011-01-01

    Data mining is becoming more and more important. The aim of this thesis is to study and research data mining, to clarify the background, knowledge and method of data mining, and research some specific areas applications. The aim is also to experiment with an open software by mining some sample data, to prove the advantage and convenience of data mining. This thesis first introduces the basic concepts of data mining, such as the definition of data mining, its basic function, common methods...

  7. Qualitative Comparison of Graph-Based and Logic-Based Multi-Relational Data Mining: A Case Study

    National Research Council Canada - National Science Library

    Ketkar, Nikhil S; Holder, Lawrence B; Cook, Diane J

    2005-01-01

    The goal of this paper is to generate insights about the differences between graph-based and logic-based approaches to multi-relational data mining by performing a case study of the graph-based system...

  8. GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia

    NARCIS (Netherlands)

    Chen, X.; Lee, G.; Maher, B. S.; Fanous, A. H.; Chen, J.; Zhao, Z.; Guo, A.; van den Oord, E.; Sullivan, P. F.; Shi, J.; Levinson, D. F.; Gejman, P. V.; Sanders, A.; Duan, J.; Owen, M. J.; Craddock, N. J.; O'Donovan, M. C.; Blackman, J.; Lewis, D.; Kirov, G. K.; Qin, W.; Schwab, S.; Wildenauer, D.; Chowdari, K.; Nimgaonkar, V.; Straub, R. E.; Weinberger, D. R.; O'Neill, F. A.; Walsh, D.; Bronstein, M.; Darvasi, A.; Lencz, T.; Malhotra, A. K.; Rujescu, D.; Giegling, I.; Werge, T.; Hansen, T.; Ingason, A.; Nöethen, M. M.; Rietschel, M.; Cichon, S.; Djurovic, S.; Andreassen, O. A.; Cantor, R. M.; Ophoff, R.; Corvin, A.; Morris, D. W.; Gill, M.; Pato, C. N.; Pato, M. T.; Macedo, A.; Gurling, H. M. D.; McQuillin, A.; Pimm, J.; Hultman, C.; Lichtenstein, P.; Sklar, P.; Purcell, S. M.; Scolnick, E.; St Clair, D.; Blackwood, D. H. R.; Kendler, K. S.; Kahn, René S.; Linszen, Don H.; van Os, Jim; Wiersma, Durk; Bruggeman, Richard; Cahn, Wiepke; de Haan, Lieuwe; Krabbendam, Lydia; Myin-Germeys, Inez; O'Donovan, Michael C.; Kirov, George K.; Craddock, Nick J.; Holmans, Peter A.; Williams, Nigel M.; Georgieva, Lyudmila; Nikolov, Ivan; Norton, N.; Williams, H.; Toncheva, Draga; Milanova, Vihra; Owen, Michael J.; Hultman, Christina M.; Lichtenstein, Paul; Thelander, Emma F.; Sullivan, Patrick; Morris, Derek W.; O'Dushlaine, Colm T.; Kenny, Elaine; Quinn, Emma M.; Gill, Michael; Corvin, Aiden; McQuillin, Andrew; Choudhury, Khalid; Datta, Susmita; Pimm, Jonathan; Thirumalai, Srinivasa; Puri, Vinay; Krasucki, Robert; Lawrence, Jacob; Quested, Digby; Bass, Nicholas; Gurling, Hugh; Crombie, Caroline; Fraser, Gillian; Kuan, Soh Leh; Walker, Nicholas; St Clair, David; Blackwood, Douglas H. R.; Muir, Walter J.; McGhee, Kevin A.; Pickard, Ben; Malloy, Pat; Maclean, Alan W.; van Beck, Margaret; Wray, Naomi R.; Macgregor, Stuart; Visscher, Peter M.; Pato, Michele T.; Medeiros, Helena; Middleton, Frank; Carvalho, Celia; Morley, Christopher; Fanous, Ayman; Conti, David; Knowles, James A.; Ferreira, Carlos Paz; Macedo, Antonio; Azevedo, M. Helena; Pato, Carlos N.; Stone, Jennifer L.; Ruderfer, Douglas M.; Kirby, Andrew N.; Ferreira, Manuel A. R.; Daly, Mark J.; Purcell, Shaun M.; Sklar, Pamela; Chambert, Kimberly; Kuruvilla, Finny; Gabriel, Stacey B.; Ardlie, Kristin; Moran, Jennifer L.; Scolnick, Edward M.

    2011-01-01

    We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed

  9. Case study: how to apply data mining techniques in a healthcare data warehouse.

    Science.gov (United States)

    Silver, M; Sakata, T; Su, H C; Herman, C; Dolins, S B; O'Shea, M J

    2001-01-01

    Healthcare provider organizations are faced with a rising number of financial pressures. Both administrators and physicians need help analyzing large numbers of clinical and financial data when making decisions. To assist them, Rush-Presbyterian-St. Luke's Medical Center and Hitachi America, Ltd. (HAL), Inc., have partnered to build an enterprise data warehouse and perform a series of case study analyses. This article focuses on one analysis, which was performed by a team of physicians and computer science researchers, using a commercially available on-line analytical processing (OLAP) tool in conjunction with proprietary data mining techniques developed by HAL researchers. The initial objective of the analysis was to discover how to use data mining techniques to make business decisions that can influence cost, revenue, and operational efficiency while maintaining a high level of care. Another objective was to understand how to apply these techniques appropriately and to find a repeatable method for analyzing data and finding business insights. The process used to identify opportunities and effect changes is described.

  10. Applying Data-mining techniques to study drought periods in Spain

    Science.gov (United States)

    Belda, F.; Penades, M. C.

    2010-09-01

    Data-mining is a technique that it can be used to interact with large databases and to help in the discovery relations between parameters by extracting information from massive and multiple data archives. Drought affects many economic and social sectors, from agricultural to transportation, going through urban water deficit and the development of modern industries. With these problems and drought geographical and temporal distribution it's difficult to find a single definition of drought. Improving the understanding of the knowledge of climatic index is necessary to reduce the impacts of drought and to facilitate quick decisions regarding this problem. The main objective is to analyze drought periods from 1950 to 2009 in Spain. We use several kinds of information, different formats, sources and transmission mode. We use satellite-based Vegetation Index, dryness index for several temporal periods. We use daily and monthly precipitation and temperature data and soil moisture data from numerical weather model. We calculate mainly Standardized Precipitation Index (SPI) that it has been used amply in the bibliography. We use OLAP-Mining techniques to discovery of association rules between remote-sensing, numerical weather model and climatic index. Time series Data- Mining techniques organize data as a sequence of events, with each event having a time of recurrence, to cluster the data into groups of records or cluster with similar characteristics. Prior climatological classification is necessary if we want to study drought periods over all Spain.

  11. Using data mining to improve student retention in HE: a case study.

    OpenAIRE

    Zhang, Ying; Oussena, Samia; Clark, Tony; Hyensook, Kim

    2010-01-01

    Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention
 (National Audition Office, 2007).
Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient in...

  12. Dietary patterns analysis using data mining method. An application to data from the CYKIDS study.

    Science.gov (United States)

    Lazarou, Chrystalleni; Karaolis, Minas; Matalas, Antonia-Leda; Panagiotakos, Demosthenes B

    2012-11-01

    Data mining is a computational method that permits the extraction of patterns from large databases. We applied the data mining approach in data from 1140 children (9-13 years), in order to derive dietary habits related to children's obesity status. Rules emerged via data mining approach revealed the detrimental influence of the increased consumption of soft dinks, delicatessen meat, sweets, fried and junk food. For example, frequent (3-5 times/week) consumption of all these foods increases the risk for being obese by 75%, whereas in children who have a similar dietary pattern, but eat >2 times/week fish and seafood the risk for obesity is reduced by 33%. In conclusion patterns revealed from data mining technique refer to specific groups of children and demonstrate the effect on the risk associated with obesity status when a single dietary habit might be modified. Thus, a more individualized approach when translating public health messages could be achieved. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  13. Population Validity for Educational Data Mining Models: A Case Study in Affect Detection

    Science.gov (United States)

    Ocumpaugh, Jaclyn; Baker, Ryan; Gowda, Sujith; Heffernan, Neil; Heffernan, Cristina

    2014-01-01

    Information and communication technology (ICT)-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy and affect. The automated…

  14. Data Mining for CRM

    Science.gov (United States)

    Thearling, Kurt

    Data Mining technology allows marketing organizations to better understand their customers and respond to their needs. This chapter describes how Data Mining can be combined with customer relationship management to help drive improved interactions with customers. An example showing how to use Data Mining to drive customer acquisition activities is presented.

  15. Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining

    Science.gov (United States)

    Grosskreutz, Henrik; Boley, Mario; Krause-Traudes, Maike

    In this paper, we investigate the application of descriptive data mining techniques, namely subgroup discovery, for the purpose of the ad-hoc analysis of election results. Our inquiry is based on the 2009 German federal Bundestag election (restricted to the City of Cologne) and additional socio-economic information about Cologne's polling districts. The task is to describe relations between socio-economic variables and the votes in order to summarize interesting aspects of the voting behavior. Motivated by the specific challenges of election data analysis we propose novel quality functions and visualizations for subgroup discovery.

  16. A Study on the application of Data Mining Methods in the analysis of Transcripts

    Directory of Open Access Journals (Sweden)

    Luis Raunheitte

    2012-06-01

    Full Text Available Schools always had an essential role in the formation of students' intellect; however, the constant incorporation of knowledge to improve techniques and technologies used in the production of goods and services has caused a major demand for highly qualified professionals and, in order to meet that need, the teaching process must understand and adapt to the profile of the students. The transcript is the most used document to measure the performance of a student. Its digital storage combined with data mining methodologies can contribute not only to the analysis of performances, but also to the identification of significant information about student

  17. Pre-stroke use of statins on stroke outcome : a meta-analysis of observational studies

    NARCIS (Netherlands)

    Cordenier, Ann; De Smedt, Ann; Brouns, Raf; Uyttenboogaart, Maarten; De Raedt, Sylvie; Luijckx, Gert-Jan; De Keyser, Jacques

    2011-01-01

    Background: Animal pre-clinical studies suggest that statins may have neuroprotective effects in acute ischaemic stroke. Statins might also increase the risk of developing haemorrhagic transformation after thrombolytic treatment. Methods: We performed a systematic review and included studies that

  18. Using Data Mining and Computational Approaches to Study Intermediate Filament Structure and Function.

    Science.gov (United States)

    Parry, David A D

    2016-01-01

    Experimental and theoretical research aimed at determining the structure and function of the family of intermediate filament proteins has made significant advances over the past 20 years. Much of this has either contributed to or relied on the amino acid sequence databases that are now available online, and the data mining approaches that have been developed to analyze these sequences. As the quality of sequence data is generally high, it follows that it is the design of the computational and graphical methodologies that are of especial importance to researchers who aspire to gain a greater understanding of those sequence features that specify both function and structural hierarchy. However, these techniques are necessarily subject to limitations and it is important that these be recognized. In addition, no single method is likely to be successful in solving a particular problem, and a coordinated approach using a suite of methods is generally required. A final step in the process involves the interpretation of the results obtained and the construction of a working model or hypothesis that suggests further experimentation. While such methods allow meaningful progress to be made it is still important that the data are interpreted correctly and conservatively. New data mining methods are continually being developed, and it can be expected that even greater understanding of the relationship between structure and function will be gleaned from sequence data in the coming years. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Study and application of data mining and data warehouse in CIMS

    Science.gov (United States)

    Zhou, Lijuan; Liu, Chi; Liu, Daxin

    2003-03-01

    The interest in analyzing data has grown tremendously in recent years. To analyze data, a multitude of technologies is need, namely technologies from the fields of Data Warehouse, Data Mining, On-line Analytical Processing (OLAP). This paper gives a new architecture of data warehouse in CIMS according to CRGC-CIMS application engineering. The data source of this architecture comes from database of CRGC-CIMS system. The data is put in global data set by extracting, filtrating and integrating, and then the data is translated to data warehouse according information request. We have addressed two advantages of the new model in CRGC-CIMS application. In addition, a Data Warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support, OLAP queries or data mining. It is important to select the right view to materialize that answer a given set of queries. In this paper, we also have designed algorithms for selecting a set of views to be materialized in a data warehouse in order to answer the most queries under the constraint of given space. First, we give a cost model for selecting materialized views. Then we give the algorithms that adopt gradually recursive method from bottom to top. We give description and realization of algorithms. Finally, we discuss the advantage and shortcoming of our approach and future work.

  20. Contrast data mining concepts, algorithms, and applications

    CERN Document Server

    Dong, Guozhu

    2012-01-01

    A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies

  1. Television viewing time among statin users and non-users. The Polish Norwegian Study (PONS

    Directory of Open Access Journals (Sweden)

    Georgeta D. Vaidean

    2017-09-01

    In conclusion, we found a higher prevalence of prolonged TV-viewing among statin users than non-users. Future studies are needed to explore innovative behavioral interventions and patient counseling strategies to reduce TV viewing among statin users.

  2. Statin treatment and risk of recurrent venous thromboembolism: a nationwide cohort study

    NARCIS (Netherlands)

    Nguyen, Cu Dinh; Andersson, Charlotte; Jensen, Thomas Bo; Gjesing, Anne; Schjerning Olsen, Anne-Marie; Malta Hansen, Carolina; Büller, Harry; Torp-Pedersen, Christian; Gislason, Gunnar H.

    2013-01-01

    Statins may decrease the risk of primary venous thromboembolism (VTE), that is, deep vein thrombosis (DVT) and pulmonary embolism (PE) but the effect of statins in preventing recurrent VTE is less clear. The aim of this study was therefore to investigate the association between statin therapy and

  3. Ensemble Data Mining Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

  4. Data mining in agriculture

    CERN Document Server

    Mucherino, Antonio; Pardalos, Panos M

    2009-01-01

    Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009.

  5. Applied data mining for business and industry

    CERN Document Server

    Giudici, Paolo

    2009-01-01

    The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications.Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.Features detailed case studies based on applied projects within industry.Incorporates discussion of data mining software, with case studies a...

  6. Continued Statin Prescriptions After Adverse Reactions and Patient Outcomes: A Cohort Study.

    Science.gov (United States)

    Zhang, Huabing; Plutzky, Jorge; Shubina, Maria; Turchin, Alexander

    2017-08-15

    Many patients discontinue statin treatment, often after having a possible adverse reaction. The risks and benefits of continued statin therapy after an adverse reaction are not known. To examine the relationship between continuation of statin therapy (any prescription within 12 months after an adverse reaction) and clinical outcomes. Retrospective cohort study. Primary care practices affiliated with 2 academic medical centers. Patients with a presumed adverse reaction to a statin between 2000 and 2011. Information on adverse reactions to statins was obtained from structured electronic medical record data or natural-language processing of narrative provider notes. The primary composite outcome was time to a cardiovascular event (myocardial infarction or stroke) or death. Most (81%) of the adverse reactions to statins were identified from the text of electronic provider notes. Among 28 266 study patients, 19 989 (70.7%) continued receiving statin prescriptions after the adverse reaction. Four years after the presumed adverse event, the cumulative incidence of the composite primary outcome was 12.2% for patients with continued statin prescriptions, compared with 13.9% for those without them (difference, 1.7% [95% CI, 0.8% to 2.7%]; P statin was prescribed after the adverse reaction, 2014 (26.5%) had a documented adverse reaction to the second statin, but 1696 (84.2%) of those patients continued receiving statin prescriptions. The risk for recurrent adverse reactions to statins could not be established for the entire sample. It was also not possible to determine whether patients actually took the statins. Continued statin prescriptions after an adverse reaction were associated with a lower incidence of death and cardiovascular events. Chinese National Key Program of Clinical Science, National Natural Science Foundation of China, and Young Scientific Research Fund of Peking Union Medical College Hospital.

  7. Gender differences in side effects and attitudes regarding statin use in the Understanding Statin Use in America and Gaps in Patient Education (USAGE) study.

    Science.gov (United States)

    Karalis, Dean G; Wild, Robert A; Maki, Kevin C; Gaskins, Ray; Jacobson, Terry A; Sponseller, Craig A; Cohen, Jerome D

    2016-01-01

    Statin therapy has been shown to reduce cardiovascular morbidity and mortality, and the benefits of statin therapy are similar for men and women. Recent studies have shown that women are less likely to be treated with statin therapy, to be on higher doses of more potent statins, and to achieve their lipid goals as compared with men. To analyze results from the Understanding Statin Use in America and Gaps in Patient Education (USAGE) survey and to assess whether women differ from men with regard to reported side effects associated with statin use, clinician and patient interactions, as well as general attitudes and preferences regarding statin use. The study population was derived from participants in the USAGE survey, a self-administered, Internet-based questionnaire. More women reported switching or stopping a statin because of side effects compared with men. New or worsening muscle symptoms were reported in 31% of women compared with 26% of men (P statins, but less likely to use alternative low-density lipoprotein cholesterol-lowering drugs. Women were more likely to be dissatisfied with their statin, with how their clinician explained their cholesterol treatment, and less adherent to their statin than men. Women are more likely to stop or switch their statin than men, and the main reason for this was new or worsening muscle symptoms. Improved communication between the clinician and the patient about the benefits and risks of statin therapy will improve adherence, lipid goal attainment, and outcomes in women with or at risk for cardiovascular disease. Copyright © 2016 National Lipid Association. Published by Elsevier Inc. All rights reserved.

  8. Data mining methods

    CERN Document Server

    Chattamvelli, Rajan

    2015-01-01

    DATA MINING METHODS, Second Edition discusses both theoretical foundation and practical applications of datamining in a web field including banking, e-commerce, medicine, engineering and management. This book starts byintroducing data and information, basic data type, data category and applications of data mining. The second chapterbriefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statisticsare discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an indepthand useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabularmethod for decision tree building is discussed. The chapter on association rules discusses popular algorithms andcompares various algorithms in summary table form. An interesting application of genetic algorithm is introduced inthe next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived...

  9. Data mining mobile devices

    CERN Document Server

    Mena, Jesus

    2013-01-01

    With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users

  10. Data mining for dummies

    CERN Document Server

    Brown, Meta S

    2014-01-01

    Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business''s entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn''t take a data scientist to gain

  11. GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia

    DEFF Research Database (Denmark)

    Chen, X; Lee, G; Maher, B S

    2011-01-01

    We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed...... bioinformatic prioritization for all the markers with P-values ¿0.05 in both data sets. In this process, we found that in the CMYA5 gene, there were two non-synonymous markers, rs3828611 and rs10043986, showing nominal significance in both the CATIE and MGS-GAIN samples. In a combined analysis of both the CATIE...... in our Irish samples and was dropped out without further investigation. The other two markers were verified in 23 other independent data sets. In a meta-analysis of all 23 replication samples (family samples, 912 families with 4160 subjects; case-control samples, 11¿380 cases and 15¿021 controls), we...

  12. Social big data mining

    CERN Document Server

    Ishikawa, Hiroshi

    2015-01-01

    Social Media. Big Data and Social Data. Hypotheses in the Era of Big Data. Social Big Data Applications. Basic Concepts in Data Mining. Association Rule Mining. Clustering. Classification. Prediction. Web Structure Mining. Web Content Mining. Web Access Log Mining, Information Extraction and Deep Web Mining. Media Mining. Scalability and Outlier Detection.

  13. Biomedical Data Mining

    NARCIS (Netherlands)

    Peek, N.; Combi, C.; Tucker, A.

    2009-01-01

    Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data

  14. Switching statins in Norway after new reimbursement policy – a nationwide prescription study

    Science.gov (United States)

    Sakshaug, Solveig; Furu, Kari; Karlstad, Øystein; Rønning, Marit; Skurtveit, Svetlana

    2007-01-01

    What is already known about this subject Use of statins is growing worldwide and costs represent a burden to public budgets. The introduction of simvastatin generics, generic substitution and price regulations have contributed to price reductions and resulted in overall cost reductions of statin use in Norway. What this study adds New reimbursement regulations for statins in Norway in June 2005, making simvastatin the drug of choice, had a great impact on physicians' prescribing of statins. Nearly 40% of the atorvastatin users switched to simvastatin during the 13-month period after implementation of the new regulations. Among the new users of statins the proportion receiving simvastatin increased from 48% in May 2005 to 92% in June 2006. The new regulations have reduced costs of statins, even though the prevalence of statin use has increased. Aims To assess the changes in prescribing of statins in Norway after implementation of the new reimbursement regulations for statins in June 2005. Methods Data were retrieved from the Norwegian Prescription Database covering the total population in Norway (4.6 million). Outcome measures were the proportion of atorvastatin users switching to simvastatin and changes in the proportion of new statin users receiving simvastatin. Based on retail costs for all statin prescriptions dispensed in Norway, expenditure was measured in Norwegian currency. Results One-year prevalences of statin use increased from 6.3 to 6.8% for women and from 7.5 to 8.1% for men from the year before to the year after the new statin regulations. Of atorvastatin users (N = 131 222), 39% switched to simvastatin during the 13-month period after the implementation. The proportion of switching was higher in women (41%) than in men (36%). In May 2005, 48% of the new statin users received simvastatin. The proportion of new users receiving simvastatin increased rapidly after implementation of the new regulations to 68% in June 2005 and reached 92% in June 2006

  15. Statin related adverse effects and patient education: a study from resource limited settings.

    Science.gov (United States)

    Mulchandani, Rubina; Lyngdoh, Tanica; Chakraborty, Praloy; Kakkar, Ashish Kumar

    2017-11-27

    Statins are the most widely prescribed class of drugs for coronary artery disease (CAD) patients and yet literature on the prevalence of statin related adverse effects (AEs) and gaps in patient education is quite limited especially in resource-limited settings of developing world. The present study was conducted to determine the prevalence of myopathy (muscle ailments) and other statin associated adverse effects among CAD patients on statin therapy. The study also aimed to assess patient perceptions, attitudes and awareness concerning the use of statins. It was a cross-sectional study conducted among 300 adult CAD patients visiting the out-patient department of a tertiary care hospital in North India, who were receiving statins for their diagnosis. An interviewer administered questionnaire was used to collect data on statin use among patients and adverse effects experienced. Myopathy or muscle related ailments like muscle pain, cramps and muscle weakness were the most prevalent (32, 34 and 47%, respectively), followed by numbness, tingling and burning in the extremities (31%). Joint pain and cognitive impairments were seen in nearly 20% of the patients. The level of awareness among participants regarding the use of statins was sub-optimal. Lack of knowledge and under-reporting of adverse effects were major concerns. The study shows that a considerable proportion of statin users experience adverse effects and knowledge and awareness amongst patients is inadequate. Awareness programmes and counselling for patients, sensitisation of healthcare professionals and better screening systems for monitoring AEs can help improve the scenario.

  16. Clinical Profile of Statin Intolerance in the Phase 3 GAUSS-2 Study.

    Science.gov (United States)

    Cho, Leslie; Rocco, Michael; Colquhoun, David; Sullivan, David; Rosenson, Robert S; Dent, Ricardo; Xue, Allen; Scott, Rob; Wasserman, Scott M; Stroes, Erik

    2016-06-01

    Recent evidence suggests that statin intolerance may be more common than reported in randomized trials. However, the statin-intolerant population is not well characterized. The goal of this report is to characterize the population enrolled in the phase 3 Goal Achievement after Utilizing an anti-PCSK9 antibody in Statin Intolerant Subjects Study (GAUSS-2; NCT 01763905). GAUSS-2 compared evolocumab, a fully human monoclonal antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9) to ezetimibe in hypercholesterolemic patients who discontinued statin therapy due to statin-associated muscle symptoms (SAMS). GAUSS-2 was a 12-week, double-blind, placebo-controlled, randomized study that enrolled patients with elevated LDL-C who were either not on a statin or able to tolerate only a low-dose due to SAMS. Patients had received ≥2 statins and were unable to tolerate any statin dose or increase in dose above a specified weekly dose due to SAMS. Three hundred seven patients (mean [SD] age, 62 [10] years; 54 % males) were randomized 2:1 (evolocumab:ezetimibe). Mean (SD) LDL-C was 4.99 (1.51) mmol/L. Patients had used ≥2 (100 %), ≥3 (55 %), or ≥4 (21 %) statins. Coronary artery disease was present in 29 % of patients. Statin-intolerant symptoms were myalgia in 80 % of patients, weakness in 39 %, and more serious complications in 20 %. In 98 % of patients, SAMS interfered with normal daily activity; in 52 %, symptoms precluded moderate exertion. Evaluation of the GAUSS-2 trial population of statin-intolerant patients demonstrates that most patients were high risk with severely elevated LDL-C and many had statin-associated muscle symptoms that interfered with their quality of life.

  17. Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras Knowledge management using data mining: a case study of the Federal University of Lavras

    Directory of Open Access Journals (Sweden)

    Olinda Nogueira Paes Cardoso

    2008-06-01

    Full Text Available A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.The management of knowledge embraces every form of production, storage, distribution and use of the knowledge, making necessary the use of information technologies to facilitate the process, due to the great increase in the volume of data. An emergent methodology that tries to solve the problem of the analysis of great amounts of data is the knowledge discovery in database (KDD and data mining, a technique that is part of this methodology. This article aims to develop, apply and analyze a tool of data mining, to extract knowledge regarding people's scientific production involved with the research at the Federal University of Lavras (Ufla. The methodology used involved bibliographical research, documental research, and method of

  18. Data mining and gap analysis for weather responsive traffic management studies.

    Science.gov (United States)

    2010-12-01

    Weather causes a variety of impacts on the transportation system. An Oak Ridge National Laboratory study estimated the : delay experienced by American drivers due to snow, ice, and fog in 1999 at 46 million hours. While severe winter storms, : hurric...

  19. Data mining and electroencephalography.

    Science.gov (United States)

    Flexer, A

    2000-08-01

    An overview of data mining (DM) and its application to the analysis of DM and electroencephalography (EEG) is given by: (i) presenting a working definition of DM, (ii) motivating why EEG analysis is a challenging field of application for DM technology and (iii) by reviewing exemplary work on DM applied to EEG analysis. The current status of work on DM and EEG is discussed and some general conclusions are drawn.

  20. A model for personnel selection with a data mining approach: A case study in a commercial bank

    Directory of Open Access Journals (Sweden)

    Adel Azar

    2013-04-01

    Full Text Available Orientation: The success or failure of an organisation has a direct relationship with how its human resources are employed and retained. Research purpose: In this paper, a decision-making tool is provided for managers to use during the recruitment process. The effective factors in employees’ performance will be identified by discovering covert patterns of the relationship between employees’ test scores and their performance at work. Motivation for the study: Large amounts of information and data on entrance evaluations and processes have been kept in organisations. There is a need to discover the pattern in the relationship between employee’s test scores and their performance at work as a tool for use during the recruitment process.? Research design, approach and method: The data mining technique that was used in this project serves as the decision tree. Rules derivation was accomplished by the Quick Unbiased and Efficient Statistical Tree(QUEST, Chi-squared Automatic Interaction Detector (CHAID,C5.0 and Classification And Regression Tree  (CART algorithm. The objective and the appropriate algorithm were determined based on seemingly ‘irrelevant’ components, which the Commerce Bank Human Resources management’s experts describe. Main finding: It was found that the ‘performance assessment’ variable was not considered as the objective. Also, it was concluded that out of 26 effective variables only five variables, such as province of employment, education level, exam score, interview score and work experience, had the most effect on the ‘promotion score’ target. Practical/managerial implication: The database and personnel information of the Commerce Bank of Iran (in 2005 and 2006 was studied and analysed as a case study in order to identify the labour factors that are effective in job performance. Appropriate and scientific employment of staff that were selected from the entrance exams of companies and

  1. A model for personnel selection with a data mining approach: A case study in a commercial bank

    Directory of Open Access Journals (Sweden)

    Adel Azar

    2013-01-01

    Full Text Available Orientation: The success or failure of an organisation has a direct relationship with how its human resources are employed and retained.Research purpose: In this paper, a decision-making tool is provided for managers to use during the recruitment process. The effective factors in employees’ performance will be identified by discovering covert patterns of the relationship between employees’ test scores and their performance at work.Motivation for the study: Large amounts of information and data on entrance evaluations and processes have been kept in organisations. There is a need to discover the pattern in the relationship between employee’s test scores and their performance at work as a tool for use during the recruitment process.?Research design, approach and method: The data mining technique that was used in this project serves as the decision tree. Rules derivation was accomplished by the Quick Unbiased and Efficient Statistical Tree(QUEST, Chi-squared Automatic Interaction Detector (CHAID,C5.0 and Classification And Regression Tree  (CART algorithm. The objective and the appropriate algorithm were determined based on seemingly ‘irrelevant’ components, which the Commerce Bank Human Resources management’s experts describe.Main finding: It was found that the ‘performance assessment’ variable was not considered as the objective. Also, it was concluded that out of 26 effective variables only five variables, such as province of employment, education level, exam score, interview score and work experience, had the most effect on the ‘promotion score’ target.Practical/managerial implication: The database and personnel information of the Commerce Bank of Iran (in 2005 and 2006 was studied and analysed as a case study in order to identify the labour factors that are effective in job performance. Appropriate and scientific employment of staff that were selected from the entrance exams of companies and organisations

  2. Data Mining and Analysis

    Science.gov (United States)

    Samms, Kevin O.

    2015-01-01

    The Data Mining project seeks to bring the capability of data visualization to NASA anomaly and problem reporting systems for the purpose of improving data trending, evaluations, and analyses. Currently NASA systems are tailored to meet the specific needs of its organizations. This tailoring has led to a variety of nomenclatures and levels of annotation for procedures, parts, and anomalies making difficult the realization of the common causes for anomalies. Making significant observations and realizing the connection between these causes without a common way to view large data sets is difficult to impossible. In the first phase of the Data Mining project a portal was created to present a common visualization of normalized sensitive data to customers with the appropriate security access. The tool of the visualization itself was also developed and fine-tuned. In the second phase of the project we took on the difficult task of searching and analyzing the target data set for common causes between anomalies. In the final part of the second phase we have learned more about how much of the analysis work will be the job of the Data Mining team, how to perform that work, and how that work may be used by different customers in different ways. In this paper I detail how our perspective has changed after gaining more insight into how the customers wish to interact with the output and how that has changed the product.

  3. Organizational Data Mining

    Science.gov (United States)

    Nemati, Hamid R.; Barko, Christopher D.

    Many organizations today possess substantial quantities of business information but have very little real business knowledge. A recent survey of 450 business executives reported that managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. To reverse this trend, businesses of all sizes would be well advised to adopt Organizational Data Mining (ODM). ODM is defined as leveraging Data Mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage. ODM has helped many organizations optimize internal resource allocations while better understanding and responding to the needs of their customers. The fundamental aspects of ODM can be categorized into Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the key distinction between ODM and Data Mining. In this chapter, we introduce ODM, explain its unique characteristics, and report on the current status of ODM research. Next we illustrate how several leading organizations have adopted ODM and are benefiting from it. Then we examine the evolution of ODM to the present day and conclude our chapter by contemplating ODM's challenging yet opportunistic future.

  4. DATA MINING AND STATISTICS METHODS USAGE FOR ADVANCED TRAINING COURSES QUALITY MEASUREMENT: CASE STUDY

    Directory of Open Access Journals (Sweden)

    Maxim I. Galchenko

    2014-01-01

    Full Text Available In the article we consider a case of the analysis of the data connected with educational statistics, namely – result of professional development courses students survey with specialized software usage. Need for expanded statistical results processing, the scheme of carrying out the analysis is shown. Conclusions on a studied case are presented. 

  5. A novel data mining method to identify assay-specific signatures in functional genomic studies

    Directory of Open Access Journals (Sweden)

    Guidarelli Jack W

    2006-08-01

    Full Text Available Abstract Background: The highly dimensional data produced by functional genomic (FG studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays. Although dimensionality reduction methods such as principal component analysis (PCA have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies. Results: The proposed method (PM is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data – a study of

  6. Reduced Risk of Barrett's Esophagus in Statin Users: Case-Control Study and Meta-Analysis.

    Science.gov (United States)

    Beales, Ian L P; Dearman, Leanne; Vardi, Inna; Loke, Yoon

    2016-01-01

    Use of statins has been associated with a reduced incidence of esophageal adenocarcinoma in population-based studies. However there are few studies examining statin use and the development of Barrett's esophagus. The purpose of this study was to examine the association between statin use and the presence of Barrett's esophagus in patients having their first gastroscopy. We have performed a case-control study comparing statin use between patients with, and without, an incident diagnosis of non-dysplastic Barrett's esophagus. Male Barrett's cases (134) were compared to 268 male age-matched controls in each of two control groups (erosive gastro-esophageal reflux and dyspepsia without significant upper gastrointestinal disease). Risk factor and drug exposure were established using standardised interviews. Logistic regression was used to compare statin exposure and correct for confounding factors. We performed a meta-analysis pooling our results with three other case-control studies. Regular statin use was associated with a significantly lower incidence of Barrett's esophagus compared to the combined control groups [adjusted OR 0.62 (95 % confidence intervals 0.37-0.93)]. This effect was more marked in combined statin plus aspirin users [adjusted OR 0.43 (95 % CI 0.21-0.89)]. The inverse association between statin or statin plus aspirin use and risk of Barrett's was significantly greater with longer duration of use. Meta-analysis of pooled data (1098 Barrett's, 2085 controls) showed that statin use was significantly associated with a reduced risk of Barrett's esophagus [pooled adjusted OR 0.63 (95 % CI 0.51-0.77)]. Statin use is associated with a reduced incidence of a new diagnosis of Barrett's esophagus.

  7. [A comparative study of maintenance services using the data-mining technique].

    Science.gov (United States)

    Cruz, Antonio M; Aguilera-Huertas, Wilmer A; Días-Mora, Darío A

    2009-08-01

    The main goal in this research was comparing two hospitals' maintenance service quality. One of them had a contract service; the other one had an in-house maintenance service. The authors followed the next stages when conducting this research: domain understanding, data characterisation and sample reduction, insight characterisation and building the TAT predictor. Multiple linear regression and clustering techniques were used for improving the efficiency of corrective maintenance tasks in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). The institution having an in-house maintenance service had better quality indicators than the contract maintenance service. There was lineal dependence between availability and service productivity.

  8. Prediction by data mining, of suicide attempts in Korean adolescents: a national study

    OpenAIRE

    Bae, Sung Man; Lee, Seung A; Lee, Seung-Hwan

    2015-01-01

    Sung Man Bae,1 Seung A Lee,2 Seung-Hwan Lee2,3 1Department of Counseling Psychology, The Cyber University of Korea, Seoul, South Korea; 2Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea; 3Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea Objective: This study aimed to develop a prediction model for suicide attempts in Korean adolescents.Methods: We conducted a decision tree analysis of 2,754 middle and hig...

  9. Statin use after acute myocardial infarction: a nationwide study in Denmark

    DEFF Research Database (Denmark)

    Rasmussen, Jeppe Nørgaard; Gislason, Gunnar H; Abildstrom, Steen Z

    2005-01-01

    AIMS: To study outpatient statin use after first acute myocardial infarction (AMI) in Denmark between 1995 and 2002 and to determine the predictors of statin use. METHODS: This is a nationwide population-based study using administrative registries. Patients with first AMI between 1995 and 2002 ol...

  10. Prediction by data mining, of suicide attempts in Korean adolescents: a national study

    Directory of Open Access Journals (Sweden)

    Bae SM

    2015-09-01

    Full Text Available Sung Man Bae,1 Seung A Lee,2 Seung-Hwan Lee2,3 1Department of Counseling Psychology, The Cyber University of Korea, Seoul, South Korea; 2Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea; 3Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea Objective: This study aimed to develop a prediction model for suicide attempts in Korean adolescents.Methods: We conducted a decision tree analysis of 2,754 middle and high school students nationwide. We fixed suicide attempt as the dependent variable and eleven sociodemographic, intrapersonal, and extrapersonal variables as independent variables.Results: The rate of suicide attempts of the total sample was 9.5%, and severity of depression was the strongest variable to predict suicide attempt. The rates of suicide attempts in the depression and potential depression groups were 5.4 and 2.8 times higher than that of the non-depression group. In the depression group, the most powerful factor to predict a suicide attempt was delinquency, and the rate of suicide attempts in those in the depression group with higher delinquency was two times higher than in those in the depression group with lower delinquency. Of special note, the rate of suicide attempts in the depressed females with higher delinquency was the highest. Interestingly, in the potential depression group, the most impactful factor to predict a suicide attempt was intimacy with family, and the rate of suicide attempts of those in the potential depression group with lower intimacy with family was 2.4 times higher than that of those in the potential depression group with higher intimacy with family. And, among the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. Finally, in the non-depression group, stress level was the most powerful factor to

  11. Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet.

    Science.gov (United States)

    Duan, Xiaoyue; Yang, Feifei; Antono, Erin; Yang, Wenge; Pianetta, Piero; Ermon, Stefano; Mehta, Apurva; Liu, Yijin

    2016-09-29

    Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd 2 Fe 14 B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For example, it shows that the surface of common Nd 2 Fe 14 B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material's overall properties.

  12. Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet

    Science.gov (United States)

    Duan, Xiaoyue; Yang, Feifei; Antono, Erin; Yang, Wenge; Pianetta, Piero; Ermon, Stefano; Mehta, Apurva; Liu, Yijin

    2016-09-01

    Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd2Fe14B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For example, it shows that the surface of common Nd2Fe14B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material’s overall properties.

  13. Data mining in the study of nuclear fuel cells; Mineria de datos en el estudio de celdas de combustible nuclear

    Energy Technology Data Exchange (ETDEWEB)

    Medina P, J. A. [Universidad Autonoma de Campeche, Av. Agustin Melgar s/n, Col. Buenavista, 24039 San Francisco de Campeche, Campeche (Mexico); Ortiz S, J. J.; Castillo, A.; Montes T, J. L.; Perusquia, R., E-mail: j.angel.mp@hotmail.com [ININ, Departamento de Sistemas Nucleares, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)

    2015-09-15

    In this paper is presented a study of data mining application in the analysis of fuel cells and their performance within a nuclear boiling water reactor. A decision tree was used to fulfill questions of the type If (condition) and Then (conclusion) to classify if the fuel cells will have good performance. The performance is measured by compliance or not of the cold shutdown margin, the rate of linear heat generation and the average heat generation in a plane of the reactor. It is assumed that the fuel cells are simulated in the reactor under a fuel reload and rod control patterns pre designed. 18125 fuel cells were simulated according to a steady-state calculation. The decision tree works on a target variable which is one of the three mentioned before. To analyze this objective, the decision tree works with a set of attribute variables. In this case, the attributes are characteristics of the cell as number of gadolinium rods, rods number with certain uranium enrichment mixed with a concentration of gadolinium, etc. The found model was able to predict the execution or not of the shutdown margin with a precision of around 95%. However, the other two variables showed lower percentages due to few learning cases of the model in which these variables were or were not achieved. Even with this inconvenience, the model is quite reliable and can be used in way coupled in optimization systems of fuel cells. (Author)

  14. Risk of new-onset diabetes mellitus during treatment with low-dose statins in Japan: A retrospective cohort study.

    Science.gov (United States)

    Kato, S; Miura, M

    2018-02-26

    The risk of new-onset diabetes mellitus (NODM) in Japanese patients using low-dose hydroxymethyl glutaryl coenzyme A reductase inhibitors (statins) has not been previously examined. The aim of this study was to assess the risk of NODM associated with use of high- and low-potency statins in Japanese patients taking low-dose statins. A retrospective cohort study of 2554 Japanese patients who started treatment with a statin was conducted. Only patients taking the same dose of the same statin were enrolled, and patients were separated into high- and low-potency statin groups. The outcome was incidence of NODM during statin treatment. The incidence rate of NODM in the cohort was 7.4% (n = 190). Kaplan-Meier survival curves showed a significantly higher rate of NODM in patients taking high-potency statins compared with those taking low-potency statins (P < .001, log-rank test). Baseline fasting plasma glucose levels, use of high-potency statins, male gender and combination treatment with calcium channel blockers, immunosuppressants or steroids were identified as factors that significantly increased the risk for NODM using Cox proportional hazard regression analysis. The use of high-potency statins at a low standard daily dose significantly increased the risk of NODM in Japanese patients compared with low-potency statins. Furthermore, clinicians should also be careful when prescribing statins in combination with steroids or immunosuppressants due to the increased risk of NODM. © 2018 John Wiley & Sons Ltd.

  15. Association between acute statin therapy, survival, and improved functional outcome after ischemic stroke: the North Dublin Population Stroke Study.

    LENUS (Irish Health Repository)

    2011-04-01

    Statins improve infarct volume and neurological outcome in animal stroke models. We investigated the relationship between statin therapy and ischemic stroke outcome in the North Dublin Population Stroke Study.

  16. Association between statin use and Bell's palsy: a population-based study.

    Science.gov (United States)

    Hung, Shih-Han; Wang, Li-Hsuan; Lin, Herng-Ching; Chung, Shiu-Dong

    2014-09-01

    Several reports mention that statin (HMG-CoA reductase inhibitor) use seems to be associated with several neurologic disorders and that the lipid-lowering effect of statins may contribute to some neural toxicity. This study aimed to evaluate the association between statin use and Bell's palsy using a population-based health insurance database. This case-control study identified 1,977 subjects with Bell's palsy as cases and 5,931 sex- and age-matched subjects without Bell's palsy as controls from the Taiwan Longitudinal Health Insurance Database 2000. Conditional logistic regressions was used to estimate the odds ratio (OR) and 95% confidence interval (CI) for previous use of statins between the cases and controls. The associations of regular and irregular statin users with Bell's palsy were further analyzed. By Chi-square test, there was a significant difference in the prevalence of statin use between cases and controls (23.2 vs. 16.4%, p Bell's palsy was significantly associated with previous regular statin use (≥60 days within 6 months) (adjusted OR: 1.46, 95% CI 1.28-1.67). However, there was no increased adjusted OR of irregular statin use (Bell's palsy.

  17. PENGGUNAAN ALGORITHMA APRIORI DATA MINING UNTUK MENGETAHUI TINGKATKESETIAAN KONSUMEN (BRAND LOYALITY TERHADAP MEREK KENDERAAN BERMOTOR (STUDI KASUS DEALER HONDA RUMBAI

    Directory of Open Access Journals (Sweden)

    Wirdah Choiriah

    2016-02-01

    Full Text Available AbstrakAlgoritma yang umum digunakan dalam proses pencarian frequent itemset (data yang paling sering muncul adalah Apriori. Tetapi Algoritma Apriori mempunyai memiliki kekurangan yaitu membutuhkan waktu yang lama dalam proses pencarian frequent itemset. Dengan memanfaatkan data Transaksi konsumen yang dihubungkan dengan pola kesetiaan konsumen terhadap merek kenderaan bermotor Honda maka pola hubungan keduanya melalui teknik data mining, association rule. Kategori profesi, jenis kelamin konsumen dan merek kenderaan bermotor di ukur dengan parameter pada tingkat ketertarikan konsumen terhadap merek kenderaan yang di sajikan. Algoritma yang digunakan adalah algoritma apriori, informasi yang ditampilkan berupa nilai support dan confidence dari masing-masing kategori.Kata kunci: data mining, association rule, data transaksi, algoritma apriori, support, confidence.AbstractThe algorithm is commonly used in the process of finding frequent itemset (data that most often comes up is Apriori. But the Apriori algorithm has a disadvantage that has take a long time in the process of finding frequent itemset. By utilizing the data consumer transactions associated with patterns of consumer loyalty to the brand Yamaha motor vehicles then their relationship patterns through data mining techniques, association rule. Professional category, gender consumers and brand of motor vehicles on the parameters measured by the level of consumer interest in the brand vehicles are at present. The algorithm used is a priori algorithm, the information displayed in the form of support and confidence values of each category.Keywords: data mining, association rule, transaction data, apriori algorithm, support, confidence.

  18. A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

    Science.gov (United States)

    Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

    2017-08-01

    The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

  19. Data mining and education.

    Science.gov (United States)

    Koedinger, Kenneth R; D'Mello, Sidney; McLaughlin, Elizabeth A; Pardos, Zachary A; Rosé, Carolyn P

    2015-01-01

    An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. © 2015 John Wiley & Sons, Ltd.

  20. Statin Therapy and Mortality in HIV-Infected Individuals; A Danish Nationwide Population-Based Cohort Study

    DEFF Research Database (Denmark)

    Rasmussen, Line; Kronborg, Gitte; Larsen, Carsten

    2013-01-01

    Recent studies have suggested that statins possess diverse immune modulatory and anti-inflammatory properties. As statins might attenuate inflammation, statin therapy has been hypothesized to reduce mortality in HIV-infected individuals. We therefore used a Danish nationwide cohort of HIV......-infected individuals to estimate the impact of statin use on mortality before and after a diagnosis of cardiovascular disease, chronic kidney disease or diabetes....

  1. Factores de éxito de un emprendimiento: Un estudio exploratorio con base en técnicas de data mining (Entrepreneurial success factors: An exploratory study based on Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    María Messina

    2015-04-01

    Full Text Available El Centro de Emprendedurismo CCEEmprende de- sarrolla, desde 2007, un programa de apoyo a emprende- dores. Para mejorar su gestión, resulta de gran importancia analizar, en forma preliminar, los emprendimientos en una de dos categorías: éxito o fracaso. En este artículo se identifican los principales factores asociados al éxito de un emprendimiento y cómo se vincu- lan para anticipar el futuro del emprendimiento. Se presenta un caso de estudio con base en los datos de una encuesta realizada a emprendedores participantes del programa, aplicando técnicas de clasificación. Las dos técnicas utilizadas de data mining son árbol de decisión y regresión logística, en ambas se obtuvieron resultados coincidentes. Los hallazgos muestran que los dos elementos más relevantes para anticipar el éxito de un emprendimiento son contar con financiamiento y que, anteriormente, la situa- ción laboral del emprendedor sea trabajador independiente. Estos primeros resultados obtenidos en el estudio de caso revelan información útil acerca de las mejores formas de apoyo al emprendedor, cómo generar incentivos al em- prendedor y la definición de herramientas o actividades que incidan favorablemente en el éxito de los emprendimientos. Si bien desde la teoría o para otras realidades existe infor- mación sobre los factores que colaboran en la determina- ción del éxito, para la realidad del Uruguay no se identifican estudios similares.   Abstract  Since 2007, the CCEE Entrepreneurship Centre has developed a supporting program for entrepreneurs. A preliminary analysis to determine if the venture was successful or a failure is made to improve the program’s management . In this article, the authors identify the main factors associated with entrepreneurship’s success, and how they can anticipate entrepreneurship’s performance. The case study is based on a survey data applied to the Entrepreneurship Program participants. The two data mining

  2. Statin Use and Breast Cancer Survival: A Nationwide Cohort Study from Finland

    Science.gov (United States)

    Murtola, Teemu J.; Visvanathan, Kala; Artama, Miia; Vainio, Harri; Pukkala, Eero

    2014-01-01

    Recent studies have suggested that statins, an established drug group in the prevention of cardiovascular mortality, could delay or prevent breast cancer recurrence but the effect on disease-specific mortality remains unclear. We evaluated risk of breast cancer death among statin users in a population-based cohort of breast cancer patients. The study cohort included all newly diagnosed breast cancer patients in Finland during 1995–2003 (31,236 cases), identified from the Finnish Cancer Registry. Information on statin use before and after the diagnosis was obtained from a national prescription database. We used the Cox proportional hazards regression method to estimate mortality among statin users with statin use as time-dependent variable. A total of 4,151 participants had used statins. During the median follow-up of 3.25 years after the diagnosis (range 0.08–9.0 years) 6,011 participants died, of which 3,619 (60.2%) was due to breast cancer. After adjustment for age, tumor characteristics, and treatment selection, both post-diagnostic and pre-diagnostic statin use were associated with lowered risk of breast cancer death (HR 0.46, 95% CI 0.38–0.55 and HR 0.54, 95% CI 0.44–0.67, respectively). The risk decrease by post-diagnostic statin use was likely affected by healthy adherer bias; that is, the greater likelihood of dying cancer patients to discontinue statin use as the association was not clearly dose-dependent and observed already at low-dose/short-term use. The dose- and time-dependence of the survival benefit among pre-diagnostic statin users suggests a possible causal effect that should be evaluated further in a clinical trial testing statins’ effect on survival in breast cancer patients. PMID:25329299

  3. Safety of statins

    Directory of Open Access Journals (Sweden)

    Debasish Maji

    2013-01-01

    Full Text Available Statins are an established class of drugs with proven efficacy in cardiovascular risk reduction. The concern over statin safety was first raised with the revelation of myopathy and rhabdomyolysis with the use of now withdrawn cerivastatin. Enhanced understanding of the mechanisms behind adverse effects of statins including an insight into the pharmacokinetic properties have minimised fear of statin use among clinicians. Studies reveal that occurrence of myopathy and rhabdomyolysis are rare 1/100000 patient-years. The risk of myopathy/rhabdomyolysis varies between statins due to varying pharmacokinetic profiles. This explains the differing abilities of statins to adverse effects and drug interaction potentials that precipitate adverse effects. Higher dose of rosuvastatin (80 mg/day was associated with proteinuria and hematuria while lower doses were devoid of such effects. Awareness of drugs interacting with statins and knowledge of certain combinations such as statin and fibrates together with monitoring of altered creatine kinase activity may greatly minimise associated adverse effects. Statins also asymptomatically raise levels of hepatic transaminases but are not correlated with hepatotoxicity. Statins are safe and well tolerated including more recent potent statins such as, rosuvastatin. The benefits of intensive statin use in cardiovascular risk reduction greatly outweigh risks. The present review discusses underlying causes of statin-associated adverse effects including management in high risk groups.

  4. Inception and deprescribing of statins in people aged over 80 years: cohort study.

    Science.gov (United States)

    Gulliford, Martin; Ravindrarajah, Rathi; Hamada, Shota; Jackson, Stephen; Charlton, Judith

    2017-11-01

    statin use over the age of 80 years is weakly evidence-based. This study aimed to estimate rates of statin inception and deprescribing by frailty level in people aged 80 years or older. a cohort of 212,566 participants aged ≥80 years was sampled from the UK Clinical Practice Research Datalink. Statin inception was defined as a first-ever prescription in a non-statin user; deprescribing was defined as a last ever statin prescription more than 6 months before the end of participant records. Rates were estimated in a time-to-event framework allowing for mortality as a competing risk. Co-variates were age, gender, frailty category and prevention type. prevalent statin use increased from 2001-5 (9.9%) to 2011-15 (49.3%). Inception of statins in never-users was low overall at 2.4% per year (95% confidence interval (CI) 2.2-2.6%) and declined with age. Deprescribing of statins in current users occurred at a rate of 5.6% (95% CI 5.4-5.9%) per year overall and increased with age, reaching 17.8% per year (95% CI 6.7-28.9%) among centenarians. Deprescribing was slightly higher for primary prevention (6.5% per year) than secondary prevention (5.2% per year) indications (P < 0.001). Deprescribing increased with frailty level being 5.0% per year in 'fit' participants and 7.1% in 'severe' frailty (P < 0.001). statin use has increased in the over 80s but deprescribing is common and increases with age and frailty level. These paradoxical findings highlight a need for better evidence to inform statin use and discontinuation for people aged ≥80 years. © The Author 2017. Published by Oxford University Press on behalf of the British Geriatrics Society.All rights reserved. For permissions, please email: journals.permissions@oup.com

  5. On data mining in context : cases, fusion and evaluation

    NARCIS (Netherlands)

    Putten, Petrus Wilhelmus Henricus van der

    2010-01-01

    Data mining can be seen as a process, with modeling as the core step. However, other steps such as planning, data preparation, evaluation and deployment are of key importance for applications. This thesis studies data mining in the context of these other steps with the goal of improving data mining

  6. Genetically Guided Statin Therapy on Statin Perceptions, Adherence, and Cholesterol Lowering: A Pilot Implementation Study in Primary Care Patients

    Directory of Open Access Journals (Sweden)

    Josephine H. Li

    2014-03-01

    Full Text Available Statin adherence is often limited by side effects. The SLCO1B1*5 variant is a risk factor for statin side effects and exhibits statin-specific effects: highest with simvastatin/atorvastatin and lowest with pravastatin/rosuvastatin. The effects of SLCO1B1*5 genotype guided statin therapy (GGST are unknown. Primary care patients (n = 58 who were nonadherent to statins and their providers received SLCO1B1*5 genotyping and guided recommendations via the electronic medical record (EMR. The primary outcome was the change in Beliefs about Medications Questionnaire, which measured patients’ perceived needs for statins and concerns about adverse effects, measured before and after SLCO1B1*5 results. Concurrent controls (n = 59 were identified through the EMR to compare secondary outcomes: new statin prescriptions, statin utilization, and change in LDL-cholesterol (LDL-c. GGST patients had trends (p = 0.2 towards improved statin necessity and concerns. The largest changes were the “need for statin to prevent sickness” (p < 0.001 and “concern for statin to disrupt life” (p = 0.006. GGST patients had more statin prescriptions (p < 0.001, higher statin use (p < 0.001, and greater decrease in LDL-c (p = 0.059 during follow-up. EMR delivery of SLCO1B1*5 results and recommendations is feasible in the primary care setting. This novel intervention may improve patients’ perceptions of statins and physician behaviors that promote higher statin adherence and lower LDL-c.

  7. Ensemble Data Mining Methods

    Science.gov (United States)

    Oza, Nikunj C.

    2004-01-01

    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  8. Data Mining SIAM Presentation

    Science.gov (United States)

    Srivastava, Ashok; McIntosh, Dawn; Castle, Pat; Pontikakis, Manos; Diev, Vesselin; Zane-Ulman, Brett; Turkov, Eugene; Akella, Ram; Xu, Zuobing; Kumaresan, Sakthi Preethi

    2006-01-01

    This viewgraph document describes the data mining system developed at NASA Ames. Many NASA programs have large numbers (and types) of problem reports.These free text reports are written by a number of different people, thus the emphasis and wording vary considerably With so much data to sift through, analysts (subject experts) need help identifying any possible safety issues or concerns and help them confirm that they haven't missed important problems. Unsupervised clustering is the initial step to accomplish this; We think we can go much farther, specifically, identify possible recurring anomalies. Recurring anomalies may be indicators of larger systemic problems. The requirement to identify these anomalies has led to the development of Recurring Anomaly Discovery System (ReADS).

  9. Data mining goes multidimensional.

    Science.gov (United States)

    Hettler, M

    1997-03-01

    The success of a healthcare organization depends on its ability to acquire, store, analyze and compare data across many parts of the enterprise, by many individuals. While relational databases have been around since the 1970s, their two-dimensional structure has limited--or made impossible--the kind of cross-dimensional trend analysis so necessary to healthcare today. Enter online analytical processing (OLAP), in which servers store data in multiple dimensions, opening a world of opportunity for data-mining across the enterprise. In this issue of HEALTHCARE INFORMATICS, we feature our first report from the National Software Testing Laboratories (NSTL) about technologies that will change the way healthcare does business. A division of The McGraw-Hill Companies, NSTL is an independent software and hardware testing lab offering services that include compatibility testing, bug testing, comparison testing, documentation evaluation and usability.

  10. STATIN SAFETY REVISITED DATA

    Directory of Open Access Journals (Sweden)

    O. M. Drapkina

    2013-01-01

    Full Text Available Questions of statins use in liver diseases are discussed. Data from clinical studies on statins safety in cardiac patients with liver diseases are presented. Features of statins use in nonalcoholic fatty liver disease, viral hepatitis C are considered separately.

  11. Statin Safety in Chinese: A Population-Based Study of Older Adults.

    Science.gov (United States)

    Li, Daniel Q; Kim, Richard B; McArthur, Eric; Fleet, Jamie L; Hegele, Robert A; Shah, Baiju R; Weir, Matthew A; Molnar, Amber O; Dixon, Stephanie; Tu, Jack V; Anand, Sonia; Garg, Amit X

    2016-01-01

    Compared to Caucasians, Chinese achieve a higher blood concentration of statin for a given dose. It remains unknown whether this translates to increased risk of serious statin-associated adverse events amongst Chinese patients. We conducted a population-based retrospective cohort study of older adults (mean age, 74 years) newly prescribed a statin in Ontario, Canada between 2002 and 2013, where 19,033 Chinese (assessed through a validated surname algorithm) were matched (1:3) by propensity score to 57,099 non-Chinese. This study used linked healthcare databases. The follow-up observation period (mean 1.1, maximum 10.8 years) was similar between groups, as were the reasons for censoring the observation period (end of follow-up, death, or statin discontinuation). Forty-seven percent (47%) of Chinese were initiated on a higher than recommended statin dose. Compared to non-Chinese, Chinese ethnicity did not associate with any of the four serious statin-associated adverse events assessed in this study [rhabdomyolysis hazard ratio (HR) 0.61 (95% CI 0.28 to 1.34), incident diabetes HR 1.02 (95% CI 0.80 to 1.30), acute kidney injury HR 0.90 (95% CI 0.72 to 1.13), or all-cause mortality HR 0.88 (95% CI 0.74 to 1.05)]. Similar results were observed in subgroups defined by statin type and dose. We observed no higher risk of serious statin toxicity in Chinese than matched non-Chinese older adults with similar indicators of baseline health. Regulatory agencies should review available data, including findings from our study, to decide if a change in their statin dosing recommendations for people of Chinese ethnicity is warranted.

  12. Patients who discontinued statin treatment: a protocol for cohort study using primary care data

    Science.gov (United States)

    Vinogradova, Yana; Coupland, Carol; Brindle, Peter; Hippisley-Cox, Julia

    2015-01-01

    Introduction Risk thresholds for using statins to prevent cardiovascular disease (CVD) have recently been lowered, so an increasing number of patients are now prescribed these drugs. Although the safety of long-term statin use has been generally established, concerns about the balance of risks and benefits of statins still exist for some medical professionals and patients, and issues concerning their side effects are occasionally widely publicised. This study will report the rates of stopping for statins and also identify any patient groups more likely to stop using statins, so possibly increasing their risk of cardiovascular events. Methods and analysis A prospective open cohort study between 1 January 2002 and 30 September 2014 will be based on the general population of people prescribed statins, using records from UK general practices contributing to the Clinical Practice Research Database (CPRD). Participants aged 25–84 years will enter the cohort on the date of their first prescription for a statin and leave on the earliest date of: a cardiovascular event; death; leaving the practice; the last practice upload date or the study end date. If there are no prescriptions within 90 days after the expected finishing date of a prescription, a patient will be defined as a stopper with the discontinuation outcome date as the expected finishing date. Rates of statin discontinuation will be calculated by calendar year, type and dose of statin, age, and morbidities. Cox proportional regression analyses will be run to identify the most important factors associated with discontinuation. Analyses will be run separately for patients without CVD (primary prevention) and with diagnosed CVD (secondary prevention). Ethics and dissemination The protocol has been reviewed and approved by Independent Scientific Advisory Committee for MHRA Database Research. The results will be published in a peer-reviewed journal. PMID:26493458

  13. GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia.

    LENUS (Irish Health Repository)

    Chen, X

    2011-11-01

    We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed bioinformatic prioritization for all the markers with P-values ≤0.05 in both data sets. In this process, we found that in the CMYA5 gene, there were two non-synonymous markers, rs3828611 and rs10043986, showing nominal significance in both the CATIE and MGS-GAIN samples. In a combined analysis of both the CATIE and MGS-GAIN samples, rs4704591 was identified as the most significant marker in the gene. Linkage disequilibrium analyses indicated that these markers were in low LD (3 828 611-rs10043986, r(2)=0.008; rs10043986-rs4704591, r(2)=0.204). In addition, CMYA5 was reported to be physically interacting with the DTNBP1 gene, a promising candidate for schizophrenia, suggesting that CMYA5 may be involved in the same biological pathway and process. On the basis of this information, we performed replication studies for these three single-nucleotide polymorphisms. The rs3828611 was found to have conflicting results in our Irish samples and was dropped out without further investigation. The other two markers were verified in 23 other independent data sets. In a meta-analysis of all 23 replication samples (family samples, 912 families with 4160 subjects; case-control samples, 11 380 cases and 15 021 controls), we found that both markers are significantly associated with schizophrenia (rs10043986, odds ratio (OR)=1.11, 95% confidence interval (CI)=1.04-1.18, P=8.2 × 10(-4) and rs4704591, OR=1.07, 95% CI=1.03-1.11, P=3.0 × 10(-4)). The results were also significant for the 22 Caucasian replication samples (rs10043986, OR=1.11, 95% CI=1.03-1.17, P=0.0026 and rs4704591, OR=1.07, 95% CI=1.02-1.11, P=0.0015). Furthermore, haplotype conditioned analyses indicated that the association

  14. Data mining applications in healthcare.

    Science.gov (United States)

    Koh, Hian Chye; Tan, Gerald

    2005-01-01

    Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.

  15. Visual data mining

    Science.gov (United States)

    Eidenberger, Horst

    2004-10-01

    This paper introduces a novel paradigm for integrated retrieval and browsing in content-based visual information retrieval systems. The proposed approach uses feature transformations and distance measures for content-based media access and similarity measurement. The first innovation is that distance space is visualised in a 3D user interface: 2D representations of media objects are shown on the image plane. The floor plane is used to show their distance relationships. Queries can interactively be defined by browsing through the 3D space and selecting media objects as positive or negative examples. Each selection operation defines hyper-clusters that are used for querying, and causes query execution and distance space adaptation in a background process. In order to help the user understanding distance space, descriptions are visualised in diagrams and associated with media objects. Changes in distance space are visualised by tree-like graphs. Furthermore, the user is enabled to select subspaces of distance space and select new distance metrics for them. This allows dealing with multiple similarity judgements in one retrieval process. The proposed components for visual data mining will be implemented in the visual information retrieval project VizIR. All VizIR components can be arbitrarily combined to sophisticated retrieval applications.

  16. Finding Gold in Data Mining

    Science.gov (United States)

    Flaherty, Bill

    2013-01-01

    Data-mining systems provide a variety of opportunities for school district personnel to streamline operations and focus on student achievement. This article describes the value of data mining for school personnel, finance departments, teacher evaluations, and in the classroom. It suggests that much could be learned about district practices if one…

  17. Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

    Science.gov (United States)

    Zhao, Chang; Song, Guojun

    2017-08-01

    Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

  18. Analysis Of Data Mining For Car Sales Sparepart Using Apriori Algorithm (Case Study: PT. IDK 1 FIELD

    Directory of Open Access Journals (Sweden)

    Khairul Ummi

    2016-10-01

    Full Text Available PT. IDK 1 is one of the branch offices honda car dealership that sells various types of variants honda matic or manual car and motorcycle parts. Any sales or goods sold will be performed by inputting the database directly connected directly to the central office. But PT. IDK 1 do not know a couple items frequently purchased parts simultaneously. When the stock of spare parts which amount is low, the office is only asking them to send the stock of spare parts from the central office without knowing that the other parts if the parts were purchased then the other parts were also purchased. It was considered difficult when restocking of goods because of the many types of auto parts. Data mining techniques have been widely used to solve the existing problems with the implementation of the algorithm one A-Priori to obtain information about the association between the product of a database transaction. Sales transaction data honda car parts at PT. IDK 1 can be reprocessed using data mining applications resulting association rules is a strong link between itemset sales of spare parts so that it can provide recommendations and facilitate restocking items in the arrangement or placement of goods related to a strong interdependence.

  19. Real world data mining applications

    CERN Document Server

    Abou-Nasr, Mahmoud; Stahlbock, Robert; Weiss, Gary M

    2014-01-01

    Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring s...

  20. Statin adherence and the risk of Parkinson's disease: A population-based cohort study.

    Science.gov (United States)

    Rozani, Violetta; Giladi, Nir; El-Ad, Baruch; Gurevich, Tanya; Tsamir, Judith; Hemo, Beatriz; Peretz, Chava

    2017-01-01

    While experimental data provided some compelling evidence on the benefits of statins on dopaminergic neurons, observational studies reported conflicting results regarding the potential of statins to effect the risk of Parkinson's disease (PD). To evaluate the association between changes in statin adherence over time and PD risk. A population-based cohort of new statin users (ages 40-79, years 1999-2012) was derived from a large Israeli healthcare services organization. Data included history of statin purchases and low density lipoprotein cholesterol (LDL-C) levels. Personal statin adherence was measured annually by the proportion of days covered (PDC). PD was detected employing a drug-tracer approach. Stratified (by sex, LDL-C levels at baseline and age) Cox proportional hazards models with time-dependent covariates were used to compute adjusted Hazard Ratio (HR) with 95%CI. The cohort included 232,877 individuals, 49.3% men. Mean age at first statin purchase was 56.5 (±9.8) years for men and 58.7 (±9.2) years for women. PDC distribution for the whole follow up period differed between men and women: medians 58.3% and 54.1% respectively. During a mean follow up of 7.6 (±3.4) years, 2,550 (1.1%) PD cases were identified. In a 1-year lagged analysis, we found no association between annual statin adherence and PD risk in all age-groups regardless of statin type and potency. Age-pooled HR (95%CI) for men and women with LDL-C levels at baseline ≤160mg/dL were: 0.99 (0.99-1.01), 1.01 (1.00-1.02); and for men and women with LDL-C >160mg/dL levels: 0.99 (0.98-1.01), 0.97 (0.98-1.01). Our findings suggest that statin adherence over time does not affect PD risk. Future studies should use large-scale cohorts and refining assessments of long-term profiles in statin adherence.

  1. Statin use and markers of immunity in the Doetinchem cohort study.

    Directory of Open Access Journals (Sweden)

    Hilda J I De Jong

    Full Text Available It has been suggested that statins can both stimulate and suppress the immune system, and thereby, may influence autoimmune diseases. Therefore, we studied effects of statins on innate and adaptive immunity, and self-tolerance by measuring serological levels of C-reactive protein (CRP, neopterin, immunoglobulin E (IgE antibodies and the presence of autoantibodies (antinuclear antibodies (ANA and IgM rheumatoid factor (RF in the general population. We conducted a nested case-control study within the population-based Doetinchem cohort. Data from health questionnaires, serological measurements and information on medication from linkage to pharmacy-dispensing records were available. We selected 332 statin users (cases and 331 non-users (controls, matched by age, sex, date of serum collection, history of cardiovascular diseases, diabetes mellitus type II and stroke. Multivariate regression analyses were performed to estimate effect of statins on the immune system. The median level of CRP in statin users (1.28 mg/L, interquartile range (IQR: 0.59-2.79 was lower than in non-users (1.62 mg/L, IQR: 0.79-3.35, which after adjustment was estimated to be a 28% lower level. We observed an inverse association between duration of statin use and CRP levels. Elevated levels of IgE (>100 IU/mL were more prevalent in statin users compared to non-users. A trend towards increased levels of IgE antibodies in statin users was observed, whereas no associations were found between statin use and levels of neopterin or the presence of autoantibodies. In this general population sub-sample, we observed an anti-inflammatory effect of statin use and a trend towards an increase of IgE levels, an surrogate marker for Th (helper 2 responses without a decrease in neopterin levels, a surrogate marker for Th1 response and/or self-tolerance. We postulate that the observed decreased inflammatory response during statin therapy may be important but is insufficient to induce loss of

  2. Antibacterial activity of statins: a comparative study of atorvastatin, simvastatin, and rosuvastatin.

    Science.gov (United States)

    Masadeh, Majed; Mhaidat, Nizar; Alzoubi, Karem; Al-Azzam, Sayer; Alnasser, Ziad

    2012-05-07

    Statins have several effects beyond their well-known antihyperlipidemic activity, which include immunomodulatory, antioxidative and anticoagulant effects. In this study, we have tested the possible antimicrobial activity of statins against a range of standard bacterial strains and bacterial clinical isolates. Minimum inhibitory concentrations (MIC) values were evaluated and compared among three members of the statins drug (atorvastatin, simvastatin, and rosuvastatin). It was revealed that statins are able to induce variable degrees of antibacterial activity with atorvastatin, and simvastatin being the more potent than rosuvastatin. Methicillin-sensitive staphylococcus aureus (MSSA), methicillin-resistant staphylococcus aureus (MRSA), vancomycin-susceptible enterococci (VSE), vancomycin-resistant enterococcus (VRE), acinetobacter baumannii, staphylococcus epidermidis, and enterobacter aerogenes, were more sensitive to both atorvastatin, and simvastatin compared to rosuvastatin. On the other hand, escherichia coli, proteus mirabilis, and enterobacter cloacae were more sensitive to atorvastatin compared to both simvastatin and rosuvastatin. Furthermore, most clinical isolates were less sensitive to statins compared to their corresponding standard strains. Our findings might raise the possibility of a potentially important antibacterial class effect for statins especially, atorvastatin and simvastatin.

  3. Antibacterial activity of statins: a comparative study of Atorvastatin, Simvastatin, and Rosuvastatin

    Directory of Open Access Journals (Sweden)

    Masadeh Majed

    2012-05-01

    Full Text Available Abstract Background Statins have several effects beyond their well-known antihyperlipidemic activity, which include immunomodulatory, antioxidative and anticoagulant effects. In this study, we have tested the possible antimicrobial activity of statins against a range of standard bacterial strains and bacterial clinical isolates. Methods Minimum inhibitory concentrations (MIC values were evaluated and compared among three members of the statins drug (atorvastatin, simvastatin, and rosuvastatin. Results It was revealed that statins are able to induce variable degrees of antibacterial activity with atorvastatin, and simvastatin being the more potent than rosuvastatin. Methicillin-sensitive staphylococcus aureus (MSSA, methicillin-resistant staphylococcus aureus (MRSA, vancomycin-susceptible enterococci (VSE, vancomycin-resistant enterococcus (VRE, acinetobacter baumannii, staphylococcus epidermidis, and enterobacter aerogenes, were more sensitive to both atorvastatin, and simvastatin compared to rosuvastatin. On the other hand, escherichia coli, proteus mirabilis, and enterobacter cloacae were more sensitive to atorvastatin compared to both simvastatin and rosuvastatin. Furthermore, most clinical isolates were less sensitive to statins compared to their corresponding standard strains. Conclusion Our findings might raise the possibility of a potentially important antibacterial class effect for statins especially, atorvastatin and simvastatin.

  4. Results from a rosuvastatin historical cohort study in more than 45,000 Dutch statin users, a PHARMO study

    NARCIS (Netherlands)

    Goettsch, W. G.; Heintjes, E. M.; Kastelein, J. J. P.; Rabelink, T. J.; Johansson, Saga; Herings, R. M. C.

    2006-01-01

    PURPOSE: Clinical benefits of statin therapy are accepted, but their safety profiles have been under scrutiny, particularly for the recently introduced statin, rosuvastatin, relating to serious adverse events involving muscle, kidney and liver. Therefore, a historical cohort study was performed to

  5. Error estimate evaluation in numerical approximations of partial differential equations: A pilot study using data mining methods

    Science.gov (United States)

    Assous, Franck; Chaskalovic, Joël

    2013-03-01

    In this Note, we propose a new methodology based on exploratory data mining techniques to evaluate the errors due to the description of a given real system. First, we decompose this description error into four types of sources. Then, we construct databases of the entire information produced by different numerical approximation methods, to assess and compare the significant differences between these methods, using techniques like decision trees, Kohonen's cards, or neural networks. As an example, we characterize specific states of the real system for which we can locally appreciate the accuracy between two kinds of finite elements methods. In this case, this allowed us to precise the classical Bramble-Hilbert theorem that gives a global error estimate, whereas our approach gives a local error estimate.

  6. Visual cues for data mining

    Science.gov (United States)

    Rogowitz, Bernice E.; Rabenhorst, David A.; Gerth, John A.; Kalin, Edward B.

    1996-04-01

    This paper describes a set of visual techniques, based on principles of human perception and cognition, which can help users analyze and develop intuitions about tabular data. Collections of tabular data are widely available, including, for example, multivariate time series data, customer satisfaction data, stock market performance data, multivariate profiles of companies and individuals, and scientific measurements. In our approach, we show how visual cues can help users perform a number of data mining tasks, including identifying correlations and interaction effects, finding clusters and understanding the semantics of cluster membership, identifying anomalies and outliers, and discovering multivariate relationships among variables. These cues are derived from psychological studies on perceptual organization, visual search, perceptual scaling, and color perception. These visual techniques are presented as a complement to the statistical and algorithmic methods more commonly associated with these tasks, and provide an interactive interface for the human analyst.

  7. Statins decrease perioperative cardiac complications in patients undergoing noncardiac vascular surgery: the Statins for Risk Reduction in Surgery (StaRRS) study.

    Science.gov (United States)

    O'Neil-Callahan, Kristin; Katsimaglis, George; Tepper, Micah R; Ryan, Jason; Mosby, Carla; Ioannidis, John P A; Danias, Peter G

    2005-02-01

    We sought to assess whether statins may decrease cardiac complications in patients undergoing noncardiac vascular surgery. Cardiovascular complications account for considerable morbidity in patients undergoing noncardiac surgery. Statins decrease cardiac morbidity and mortality in patients with coronary disease, and the beneficial treatment effect is seen early, before any measurable increase in coronary artery diameter. A retrospective study recorded patient characteristics, past medical history, and admission medications on all patients undergoing carotid endarterectomy, aortic surgery, or lower extremity revascularization over a two-year period (January 1999 to December 2000) at a tertiary referral center. Recorded perioperative complication outcomes included death, myocardial infarction, ischemia, congestive heart failure, and ventricular tachyarrhythmias occurring during the index hospitalization. Univariate and multivariate logistic regressions identified predictors of perioperative cardiac complications and medications that might confer a protective effect. Complications occurred in 157 of 1,163 eligible hospitalizations and were significantly fewer in patients receiving statins (9.9%) than in those not receiving statins (16.5%, p = 0.001). The difference was mostly accounted by myocardial ischemia and congestive heart failure. After adjusting for other significant predictors of perioperative complications (age, gender, type of surgery, emergent surgery, left ventricular dysfunction, and diabetes mellitus), statins still conferred a highly significant protective effect (odds ratio 0.52, p = 0.001). The protective effect was similar across diverse patient subgroups and persisted after accounting for the likelihood of patients to have hypercholesterolemia by considering their propensity to use statins. Use of statins was highly protective against perioperative cardiac complications in patients undergoing vascular surgery in this retrospective study.

  8. Patient co-payment and adherence to statins: a review and case studies.

    Science.gov (United States)

    Simoens, Steven; Sinnaeve, Peter R

    2014-02-01

    This study aims to review the international literature about whether there is an association between co-payment and statin adherence, and to present case studies to illustrate the impact of a reduction in patient co-payment associated with generic drugs on improving therapy adherence. Studies that examined the impact of patient co-payment on statin adherence were identified in PubMed, Cochrane Central Register of Controlled Trials and EconLit up to January 2013. A standardized data extraction form was completed for each included study, collecting information about country, sample, setting, adherence measure, design, results about the impact of co-payment on statin adherence, and methodological quality. Two cases from the outpatient clinic of one the authors (PRS) were added. The literature supported a statistically significant negative association between co-payment and statin adherence. This association appeared to be influenced by the absolute level of co-payments, the size of the co-payment change, whether co-payment increases or decreases, the time horizon over which the impact of a co-payment change is examined, the type of drug for which co-payment changes (e.g. generic or branded drug), the availability of alternative drugs and switching behaviour. Two case studies illustrated that cost issues are important to patients and that patient adherence to statin therapy improved following a switch to generic statins. Current studies have demonstrated that statin adherence is influenced by co-payment and a range of patient, physician and pharmacy characteristics. Nevertheless, the power of these models to explain the variation in adherence remains limited.

  9. Data preprocessing in data mining

    CERN Document Server

    García, Salvador; Herrera, Francisco

    2015-01-01

    Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying t...

  10. Privacy Preserving Distributed Data Mining

    Data.gov (United States)

    National Aeronautics and Space Administration — Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring...

  11. Learning data mining with R

    CERN Document Server

    Makhabel, Bater

    2015-01-01

    This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

  12. Data mining for social network data

    CERN Document Server

    Memon, Nasrullah; Hicks, David L; Chen, Hsinchun

    2010-01-01

    Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on ""Data Mining for Social Network Data"" will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, and activities in open fora, and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution

  13. Eligibility for Statin Treatment in Korean Subjects with Reduced Renal Function: An Observational Study

    Directory of Open Access Journals (Sweden)

    Byung Sub Moon

    2016-09-01

    Full Text Available BackgroundThe purpose of this study was to investigate the relationship between statin eligibility and the degree of renal dysfunction using the Adult Treatment Panel (ATP III and the American College of Cardiology (ACC/American Heart Association (AHA guidelines in Korean adults.MethodsRenal function was assessed in 18,746 participants of the Kangbuk Samsung Health Study from January 2011 to December 2012. Subjects were divided into three groups according to estimated glomerular filtration rate (eGFR: stage 1, eGFR ≥90 mL/min/1.73 m2; stage 2, eGFR 60 to 89 mL/min/1.73 m2; and stages 3 to 5, eGFR <60 mL/min/1.73 m2. Statin eligibility in these groups was determined using the ATP III and ACC/AHA guidelines, and the risk for 10-year atherosclerotic cardiovascular disease (ASCVD was calculated using the Framingham Risk Score (FRS and Pooled Cohort Equation (PCE.ResultsThere were 3,546 (18.9% and 4,048 (21.5% statin-eligible subjects according to ATP III and ACC/AHA guidelines, respectively. The proportion of statin-eligible subjects increased as renal function deteriorated. Statin eligibility by the ACC/AHA guidelines showed better agreement with the Kidney Disease Improving Global Outcomes (KDIGO recommendations compared to the ATP III guidelines in subjects with stage 3 to 5 chronic kidney disease (CKD (κ value, 0.689 vs. 0.531. When the 10-year ASCVD risk was assessed using the FRS and PCE, the mean risk calculated by both equations significantly increased as renal function declined.ConclusionsThe proportion of statin-eligible subjects significantly increased according to worsening renal function in this Korean cohort. ACC/AHA guideline showed better agreement for statin eligibility with that recommended by KDIGO guideline compared to ATP III in subjects with CKD.

  14. A Data mining and Meta-Analysis of Geodiversity and Geological Preservation Studies: Pedodiversity, the Other Side of the Coin

    Science.gov (United States)

    Ibáñez, Juanjo; Brevik, Eric C.; Cerdà, Artemi

    2017-04-01

    Land degradation processes are complex and diverse (Yan and Cai, 2015; Álvarez-Martínez et al., 2016; Zhang et al., 2016). To understand the processes of degradation there is the need to understand a wide range of factors, including the sociological, biological, hydrological and geological components of the ecosystems (de Araujo et al., 2015; Li et al., 2016; Muñoz Rojas et al., 2016; Rodrigo Comino et al., 2016). Although the most widely accepted definitions of geodiversity include geology, landforms and soils, in practice published studies on the preservation of geodiversity typically ignore the soil resource and pay more attention to other items, while many others do not include soils in such definitions. Soils are a key component of the Earth system as they regulate the hydrological, erosional, biological and geochemical Earth cycles (Keesstra et al., 2012; Brevik et al., 2015), offer resources, goods and services to human societies (Mol and Keesstra, 2012), and are a key issue in the United Nations goals to achieve sustainability (Keesstra et al., 2016). The preservation of pedodiversity is practically ignored by governments. The same can be said in the acceptance of geoparks by UNESCO, when soils are ignored. A few researchers have paid attention to geodiversity (Ibáñez et al., 2016; Stavi et al., 2016). In this study a data mining and metanalysis of this topic has been carried out using four different sources; Google, Google Scholar, Scopus, and the contents of Geoheritage journal. The results obtained show the same trends in all four studied sources. Soil resources are neglected in geodiversity studies as well as in the preservation of geological heritage against the scientific rationality inherent in the definition of geodiversity. Furthermore, pedodiversity studies have been carried out by a small set of interested pedologists following the same conceptual frames and mathematical tools reaching interesting universal patterns and a common language

  15. DATA MINING AND APPLICATION OF IT TO CAPITAL MARKETS

    Directory of Open Access Journals (Sweden)

    Cenk AKKAYA

    2011-07-01

    Full Text Available Nowadays with the development of technology importance given to knowledge increases gradually. Data mining enables to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field it is a specially used in financial performance applications, guessing the enterprise bankruptcies and failures, determining transaction manipulation, determining financial risk management, determining customer profile and depth management. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining applications to capital markets.

  16. Statin use and risk of prostate cancer: a Danish population-based case-control study, 1997-2010.

    Science.gov (United States)

    Jespersen, Christina G; Nørgaard, Mette; Friis, Søren; Skriver, Charlotte; Borre, Michael

    2014-02-01

    Conflicting evidence has suggested that statins possess chemopreventive properties against prostate cancer (PCa). Therefore, we examined the association between statin use and risk of PCa in a Denmark-based case-control study. We identified 42,480 patients diagnosed with incident PCa during 1997-2010 from a national cancer registry. Five age-matched population controls (n=212,400) were selected for each case using risk-set sampling. Statin use from 1996 to the index date was obtained from the National Prescription Registry. Odds ratios (ORs) adjusted for age, comorbidity, non-steroidal anti-inflammatory drug use, and educational level for PCa associated with statin use, were computed using conditional logistic regression. Analyses were stratified by duration of statin use (0-1, 2-4, 5-9, or ≥10 years), stage of PCa (localized or advanced), and type of statin used (lipophilic or hydrophilic). In total, 7915 patients (19%) and 39,384 controls (19%) redeemed statin prescriptions prior to the index date. Overall, statin users had a 6% lower risk of PCa compared with non-users [adjusted OR (ORa), 0.94; 95% confidence interval (CI), 0.91-0.97]. Risk estimates did not differ substantially by duration or type of statin used. Slightly larger statin use-associated risk reductions were observed for advanced PCa (ORa, 0.90; 95% CI, 0.85-0.96) and with statin use ≥10 years (ORa, 0.78; 95% CI, 0.65-0.95). Statin use was associated with a risk reduction overall (6%) and, specifically with advanced PCa (10%). Differences in diagnostic measures and residual confounding by socioeconomic parameters may have influenced our results. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Effect of an increased dosage of statins on spinal degenerative joint disease: a retrospective cohort study.

    Science.gov (United States)

    Cheng, Yuan-Yang; Kao, Chung-Lan; Lin, Shih-Yi; Chang, Shin-Tsu; Wei, Tz-Shiang; Chang, Shih-Ni; Lin, Ching-Heng

    2018-02-08

    It has been proven that statin can protect synovial joints from developing osteoarthritis through its anti-inflammatory effects. However, studies on the effect of statins on spinal degenerative joint diseases are few and limited to in vitro studies. Therefore, we investigated the relationship between the statin dosage and the development of spinal degenerative joint diseases. A retrospective cohort study. Patients registered in Taiwan National Health Insurance Research Database. Patients aged 40-65 years old from 2001 to 2010 were included. Those who received statin treatment before 2001, were diagnosed with spinal degenerative joint diseases or received any spinal surgery before 2004 or had any spinal trauma before 2011 were excluded. A total of 7238 statin users and 164 454 non-users were identified and followed up for the next 7 years to trace the development of spinal degenerative joint disease. The incident rate of spinal degenerative joint diseases and HRs among the groups treated with different statin dosages. A higher dosage of statins was associated with a significantly lower risk of developing spinal degenerative joint disease in patients with hypercholesterolaemia. Compared with the group receiving less than 5400 mg of a statin, the HR of the 11 900-28 000 mg group was 0.83 (95% CI 0.70 to 0.99), and that of the group receiving more than 28 000 mg was 0.81 (95% CI 0.68 to 0.97). Results of subgroup analysis showed a significantly lower risk in men, those aged 50-59 years and those with a monthly income less than US$600. Our study's findings clearly indicated that a higher dosage of statins can reduce the incidence of spinal degenerative joint disease in patients with hypercholesterolaemia, and it can be beneficial for people with a higher risk of spine degeneration. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise

  18. Educational Data Mining Acceptance among Undergraduate Students

    Science.gov (United States)

    Wook, Muslihah; Yusof, Zawiyah M.; Nazri, Mohd Zakree Ahmad

    2017-01-01

    The acceptance of Educational Data Mining (EDM) technology is on the rise due to, its ability to extract new knowledge from large amounts of students' data. This knowledge is important for educational stakeholders, such as policy makers, educators, and students themselves to enhance efficiency and achievements. However, previous studies on EDM…

  19. Data mining for environmental analysis and diagnostic: a case study of upwelling ecosystem of Arraial do Cabo

    Directory of Open Access Journals (Sweden)

    Gilberto Carvalho Pereira

    2008-03-01

    Full Text Available The Brazilian coastal zone presents a large extension and a variety of environments. Nevertheless, little is known about biological diversity and ecosystem dynamics. Environmental changes always occur; however, it is important to distinguish natural from anthropic variability. Under these scenarios, the aim of this work is to present a Data Mining methodology able to access the quality and health levels of the environmental conditions through the biological integrity concept. A ten-year time series of physical, chemical and biological parameters from an influenced upwelling area of Arraial do Cabo-RJ were used to generate a classification model based on association rules. The model recognizes seven different classes of water based on biological diversity and a new trophic index (PLIX. Artificial neural networks were evolved and optimized by genetic algorithms to forecast these indices, enabling environmental diagnostic to be made taking into account control mechanisms of topology, stability and complex behavioral properties of food web.A zona costeira brasileira apresenta grande extensão e variedade de ambientes. Contudo, pouco se sabe sobre sua diversidade biológica e o funcionamento dos ecossistemas. Como mudanças ambientais são constantes, é muito importante distinguir entre variabilidade natural e antrópica. Nesse cenário, o objetivo deste trabalho é apresentar a metodologia para o desenvolvimento de um Sistema Inteligente de Gerenciamento Integrado do Ecossistema Costeiro (SIGIEC capaz de acessar o nível de qualidade e saúde ambiental através do conceito de Integridade Biológica. Foram usadas séries temporais de dez anos de parâmetros físicos, químicos e biológicos para extrair conhecimento e gerar modelos de regras de associação para classificar sete diferentes tipos de condições ambientais, analisadas através da diversidade biológica e um novo índice trófico (PLIX. Redes neurais artificiais foram otimizadas por

  20. Exposure to statins and risk of common cancers: a series of nested case-control studies

    Directory of Open Access Journals (Sweden)

    Coupland Carol

    2011-09-01

    Full Text Available 1 Abstract Background Many studies and meta-analyses have investigated the effects of statins on cancer incidence but without showing consistent effects. Methods A series of nested case-control studies was conducted covering 574 UK general practices within the QResearch database. Cases were patients with primary cancers diagnosed between 1998 and 2008. The associations between statin use and risk of ten site-specific cancers were estimated with conditional logistic regression adjusted for co-morbidities, smoking status, socio-economic status, and use of non-steroidal anti-inflammatory drugs, cyclo-oxygenase-2 inhibitors and aspirin. Results 88125 cases and 362254 matched controls were analysed. The adjusted odds ratio for any statin use and cancer at any site were 1.01 (95%CI 0.99 to 1.04. For haematological malignancies there was a significant reduced risk associated with any statin use (odds ratio 0.78, 95%CI 0.71 to 0.86. Prolonged (more than 4 years use of statins was associated with a significantly increased risk of colorectal cancer (odds ratio 1.23, 95%CI 1.10 to 1.38, bladder cancer (odds ratio 1.29, 95%CI 1.08 to 1.54 and lung cancer (odds ratio 1.18, 95%CI 1.05 to 1.34. There were no significant associations with any other cancers. Conclusion In this large population-based case-control study, prolonged use of statins was not associated with an increased risk of cancer at any of the most common sites except for colorectal cancer, bladder cancer and lung cancer, while there was a reduced risk of haematological malignancies.

  1. Big Data Mining: Tools & Algorithms

    Directory of Open Access Journals (Sweden)

    Adeel Shiraz Hashmi

    2016-03-01

    Full Text Available We are now in Big Data era, and there is a growing demand for tools which can process and analyze it. Big data analytics deals with extracting valuable information from that complex data which can’t be handled by traditional data mining tools. This paper surveys the available tools which can handle large volumes of data as well as evolving data streams. The data mining tools and algorithms which can handle big data have also been summarized, and one of the tools has been used for mining of large datasets using distributed algorithms.

  2. PROGRAMS WITH DATA MINING CAPABILITIES

    Directory of Open Access Journals (Sweden)

    Ciobanu Dumitru

    2012-03-01

    Full Text Available The fact that the Internet has become a commodity in the world has created a framework for anew economy. Traditional businesses migrate to this new environment that offers many features and options atrelatively low prices. However competitiveness is fierce and successful Internet business is tied to rigorous use of allavailable information. The information is often hidden in data and for their retrieval is necessary to use softwarecapable of applying data mining algorithms and techniques. In this paper we want to review some of the programswith data mining capabilities currently available in this area.We also propose some classifications of this softwareto assist those who wish to use such software.

  3. Data mining methods and applications

    CERN Document Server

    Lawrence, Kenneth D; Klimberg, Ronald K

    2007-01-01

    With today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management tools and methodology optimization Gain sophisticated breakdowns and complex analysis through multivariate, evolutionary, and neural net methodsLearn how to classify data and maintain qualityTransform Data into Business Acumen Data Mining Methods and

  4. Risk of Intracranial Hemorrhage From Statin Use in Asians: A Nationwide Cohort Study.

    Science.gov (United States)

    Chang, Chia-Hsuin; Lin, Chin-Hsien; Caffrey, James L; Lee, Yen-Chieh; Liu, Ying-Chun; Lin, Jou-Wei; Lai, Mei-Shu

    2015-06-09

    Reports of statin usage and increased risk of intracranial hemorrhage (ICH) have been inconsistent. This study examined potential associations between statin usage and the risk of ICH in subjects without a previous history of stroke. Patients initiating statin therapy between 2005 and 2009 without a previous history of ischemic or hemorrhagic stroke were identified from Taiwan's National Health Insurance database. Participants were stratified by advanced age (≥70 years), sex, and diagnosed hypertension. The outcome of interest was hospital admission for ICH (International Classification of Diseases, Ninth Revision, Clinical Modification codes 430, 431, 432). Cox regression models were applied to estimate the hazard ratio of ICH. The cumulative statin dosage stratified by quartile and adjusted for baseline disease risk score served as the primary variable using the lowest quartile of cumulative dosage as a reference. There were 1 096 547 statin initiators with an average follow-up of 3.3 years. The adjusted hazard ratio for ICH between the highest and the lowest quartile was nonsignificant at 1.06 with a 95% confidence interval spanning 1.00 (0.94-1.19). Similar nonsignificant results were found in sensitivity analyses using different outcome definitions or model adjustments, reinforcing the robustness of the study findings. Subgroup analysis identified an excess of ICH frequency in patients without diagnosed hypertension (adjusted hazard ratio 1.36 [1.11-1.67]). In general, no association was observed between cumulative statin use and the risk of ICH among subjects without a previous history of stroke. An increased risk was identified among the nonhypertensive cohort, but this finding should be interpreted with caution. © 2015 American Heart Association, Inc.

  5. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins

    NARCIS (Netherlands)

    Postmus, Iris; Trompet, Stella; Deshmukh, Harshal A.; Barnes, Michael R.; Li, Xiaohui; Warren, Helen R.; Chasman, Daniel I.; Zhou, Kaixin; Arsenault, Benoit J.; Donnelly, Louise A.; Wiggins, Kerri L.; Avery, Christy L.; Griffin, Paula; Feng, QiPing; Taylor, Kent D.; Li, Guo; Evans, Daniel S.; Smith, Albert V.; de Keyser, Catherine E.; Johnson, Andrew D.; de Craen, Anton J. M.; Stott, David J.; Buckley, Brendan M.; Ford, Ian; Westendorp, Rudi G. J.; Slagboom, P. Eline; Sattar, Naveed; Munroe, Patricia B.; Sever, Peter; Poulter, Neil; Stanton, Alice; Shields, Denis C.; O'Brien, Eoin; Shaw-Hawkins, Sue; Chen, Y.-D. Ida; Nickerson, Deborah A.; Smith, Joshua D.; Dubé, Marie Pierre; Boekholdt, S. Matthijs; Hovingh, G. Kees; Kastelein, John J. P.; McKeigue, Paul M.; Betteridge, John; Neil, Andrew; Durrington, Paul N.; Doney, Alex; Carr, Fiona; Morris, Andrew; McCarthy, Mark I.; Groop, Leif; Ahlqvist, Emma; Bis, Joshua C.; Rice, Kenneth; Smith, Nicholas L.; Lumley, Thomas; Whitsel, Eric A.; Stürmer, Til; Boerwinkle, Eric; Ngwa, Julius S.; O'Donnell, Christopher J.; Vasan, Ramachandran S.; Wei, Wei-Qi; Wilke, Russell A.; Liu, Ching-Ti; Sun, Fangui; Guo, Xiuqing; Heckbert, Susan R.; Post, Wendy; Sotoodehnia, Nona; Arnold, Alice M.; Stafford, Jeanette M.; Ding, Jingzhong; Herrington, David M.; Kritchevsky, Stephen B.; Eiriksdottir, Gudny; Launer, Leonore J.; Harris, Tamara B.; Chu, Audrey Y.; Giulianini, Franco; Macfadyen, Jean G.; Barratt, Bryan J.; Nyberg, Fredrik; Stricker, Bruno H.; Uitterlinden, André G.; Hofman, Albert; Rivadeneira, Fernando; Emilsson, Valur; Franco, Oscar H.; Ridker, Paul M.; Gudnason, Vilmundur; Liu, Yongmei; Denny, Joshua C.; Ballantyne, Christie M.; Rotter, Jerome I.; Adrienne Cupples, L.; Psaty, Bruce M.; Palmer, Colin N. A.; Tardif, Jean-Claude; Colhoun, Helen M.; Hitman, Graham; Krauss, Ronald M.; Wouter Jukema, J.; Caulfield, Mark J.; Donnelly, Peter; Barroso, Ines; Blackwell, Jenefer M.; Bramon, Elvira; Brown, Matthew A.; Casas, Juan P.; Corvin, Aiden; Deloukas, Panos; Duncanson, Audrey; Jankowski, Janusz; Markus, Hugh S.; Mathew, Christopher G.; Plomin, Robert; Rautanen, Anna; Sawcer, Stephen J.; Trembath, Richard C.; Viswanathan, Ananth C.; Wood, Nicholas W.; Spencer, Chris C. A.; Band, Gavin; Bellenguez, Céline; Freeman, Colin; Hellenthal, Garrett; Giannoulatou, Eleni; Pirinen, Matti; Pearson, Richard; Strange, Amy; Su, Zhan; Vukcevic, Damjan; Langford, Cordelia; Hunt, Sarah E.; Edkins, Sarah; Gwilliam, Rhian; Blackburn, Hannah; Bumpstead, Suzannah J.; Dronov, Serge; Gillman, Matthew; Gray, Emma; Hammond, Naomi; Jayakumar, Alagurevathi; McCann, Owen T.; Liddle, Jennifer; Potter, Simon C.; Ravindrarajah, Radhi; Ricketts, Michelle; Waller, Matthew; Weston, Paul; Widaa, Sara; Whittaker, Pamela

    2014-01-01

    Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol

  6. Integrating Data Mining Techniques into Telemedicine Systems

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2014-01-01

    Full Text Available The medical system is facing a wide range of challenges nowadays due to changes that are taking place in the global healthcare systems. These challenges are represented mostly by economic constraints (spiraling costs, financial issues, but also, by the increased emphasis on accountability and transparency, changes that were made in the education field, the fact that the biomedical research keeps growing in what concerns the complexities of the specific studies etc. Also the new partnerships that were made in medical care systems and the great advances in IT industry suggest that a predominant paradigm shift is occurring. This needs a focus on interaction, collaboration and increased sharing of information and knowledge, all of these may is in turn be leading healthcare organizations to embrace the techniques of data mining in order to create and sustain optimal healthcare outcomes. Data mining is a domain of great importance nowadays as it provides advanced data analysis techniques for extracting the knowledge from the huge volumes of data collected and stored by every system of a daily basis. In the healthcare organizations data mining can provide valuable information for patient's diagnosis and treatment planning, customer relationship management, organization resources management or fraud detection. In this article we focus on describing the importance of data mining techniques and systems for healthcare organizations with a focus on developing and implementing telemedicine solution in order to improve the healthcare services provided to the patients. We provide architecture for integrating data mining techniques into telemedicine systems and also offer an overview on understanding and improving the implemented solution by using Business Process Management methods.

  7. Mastering SQL Server 2014 data mining

    CERN Document Server

    Bassan, Amarpreet Singh

    2014-01-01

    If you are a developer who is working on data mining for large companies and would like to enhance your knowledge of SQL Server Data Mining Suite, this book is for you. Whether you are brand new to data mining or are a seasoned expert, you will be able to master the skills needed to build a data mining solution.

  8. Statin prescribing in Australia: socioeconomic and sex differences. A cross-sectional study.

    Science.gov (United States)

    Stocks, Nigel P; Ryan, Philip; McElroy, Heather; Allan, James

    2004-03-01

    To assess if there are any differences in statin prescribing across Australia by socioeconomic status or sex and to relate prescribing rates to coronary heart disease (CHD) mortality rates. Cross-sectional study using data on statin prescribing by age, sex and patient postcode for the period May to December 2002. The Australian population, stratified by sex and quintile of Index of Relative Socio-Economic Disadvantage (IRSD). Age-standardised rates of statin scripts per 1000 population per month for each sex and IRSD quintile. 9.1 million prescriptions for statins were supplied between May and December 2002, for a total cost of 570 million dollars. The age-standardised rates for statin prescribing in women varied from 56.9 (95% CI, 56.6-57.2) scripts per 1000 population per month in the most disadvantaged socioeconomic quintile through 53.4 (95% CI, 53.0-53.7), 50.3 (95% CI, 50.0-50.6), 48.4 (95% CI, 48.1-48.7) to 46.3 (95% CI, 46.0-46.6) in the least disadvantaged quintile. For men the figures were 52.6 (95% CI, 52.3-52.9), 50.9 (95% CI, 50.6-51.2), 48.8 (95% CI, 48.6-49.1), 47.7 (95% CI, 47.4-47.9), and 51.9 (95% CI, 51.6-52.2). There was a significant linear association between statin prescribing and CHD mortality by quintile of socioeconomic disadvantage in women (weighted least squares slope, 0.380; 95% CI, 0.366 to 0.395; P < 0.0001), but not in men (slope, -0.002; 95% CI, -0.010 to 0.006; P = 0.65). Our results suggest that in men there is either overprescribing of statins in the highest socioeconomic quintile or underprescribing in the lowest. Furthermore, contrary to expectation, women - relative to men - are prescribed statins at higher rates at lower levels of risk (using CHD deaths as a proxy measure of risk).

  9. Modeling Techniques for Data Mining

    Czech Academy of Sciences Publication Activity Database

    Řezanková, H.; Húsek, Dušan

    2000-01-01

    Roč. 8, č. 3 (2000), s. 125-132 ISSN 0572-3043 R&D Projects: GA ČR GA201/97/0885 Institutional research plan: AV0Z1030915 Keywords : data mining * discriminant analysis * logistic regression * decision trees * neural networks * general unary hypotheses automaton Subject RIV: BB - Applied Statistics, Operational Research

  10. A Tools-Based Approach to Teaching Data Mining Methods

    Science.gov (United States)

    Jafar, Musa J.

    2010-01-01

    Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business…

  11. A mixed-method approach to study lived experiences of high cholesterol and statin use in Denmark

    DEFF Research Database (Denmark)

    Lau, Sofie Rosenlund

    2014-01-01

    ,000 Danes are being treated with statins for the purpose of lowering their blood cholesterol hereby decreasing the future risk of cardiovascular disease. At a global level, however, guidelines for the prescription of statins for primary prevention are often clouded in scientific controversy. Not only...... as a cultural phenomenon. With statins as my point of departure I study how contemporary preventive health initiatives has transformed along with new technoscientific innovations, changes in political and financial interests and a shift from focusing on the diseased body to the body at risk (e.g. Rose 2007...... and practices in regard to high cholesterol and statins in the Danish population aged 45-75. The latter being a collaborative with colleagues from public health, general practice and humanities. But how do these different methodologies interfere with my research object - cholesterol and statin use? And how can...

  12. Application and Exploration of Big Data Mining in Clinical Medicine

    Science.gov (United States)

    Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

    2016-01-01

    Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378

  13. The chondroprotective effects of intraarticular application of statin in osteoarthritis: An experimental study

    Directory of Open Access Journals (Sweden)

    Sarp Bayyurt

    2015-01-01

    Full Text Available Background: Osteoarthritis (OA is the most frequent chronic joint disease causing pain and disability. Recent reports have shown that statin may have the potential to inhibit osteoarthritis. This study of early stage OA developed in an experimental rabbit model, aimed to evaluate the chondroprotective effects of intraarticularly applied atorvastatin on cartilage tissue macroscopically and histopathologically by examining intracellular and extracellular changes by light and electron microscope. Materials and Methods: The experimental knee OA model was created by cutting the anterior cruciate ligament of the 20 mature New Zealand rabbits. The rabbits were randomly allocated into two groups of 10. Study group: The group that received intraarticular statin therapy; Control group: The group that did not receive any intraarticular statin therapy. The control group received an intraarticular administration of saline and the study group atorvastatin from the 1st week postoperatively, once a week for 3 weeks. The knee joints were removed including the femoral and tibial joint surfaces for light and electron microscopic studies of articular cartilages. Results: The mean total points obtained from the evaluation of the lesions that developed in the medial femoral condyle were 11.33 ± 0.667 for the control group and 1.5 ± 0.687 for the study group. The mean total points obtained from the evaluation of the lesions that developed in medial tibial plateau cartilage tissue were 11.56 ± 0.709 for the control group and 1.40 ± 0.618 for the study group. Electron microscopic evaluation revealed healthy cartilage tissue with appropriate chondrocyte and matrix structure in study group and impaired cartilage tissue in control group. Conclusion: Chondroprotective effect of statin on cartilage tissue was determined in this experimental OA model evaluated macroscopically and by light and electron microscope. There are some evidences to believe that the

  14. A Data Mining Classification Approach for Behavioral Malware Detection

    Directory of Open Access Journals (Sweden)

    Monire Norouzi

    2016-01-01

    Full Text Available Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.

  15. Research on Customer Value Based on Extension Data Mining

    Science.gov (United States)

    Chun-Yan, Yang; Wei-Hua, Li

    Extenics is a new discipline for dealing with contradiction problems with formulize model. Extension data mining (EDM) is a product combining Extenics with data mining. It explores to acquire the knowledge based on extension transformations, which is called extension knowledge (EK), taking advantage of extension methods and data mining technology. EK includes extensible classification knowledge, conductive knowledge and so on. Extension data mining technology (EDMT) is a new data mining technology that mining EK in databases or data warehouse. Customer value (CV) can weigh the essentiality of customer relationship for an enterprise according to an enterprise as a subject of tasting value and customers as objects of tasting value at the same time. CV varies continually. Mining the changing knowledge of CV in databases using EDMT, including quantitative change knowledge and qualitative change knowledge, can provide a foundation for that an enterprise decides the strategy of customer relationship management (CRM). It can also provide a new idea for studying CV.

  16. Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study.

    Directory of Open Access Journals (Sweden)

    Ann-Katrin Rother

    Full Text Available Clinical symptoms in children with pulmonary diseases are frequently non-specific. Rare diseases such as primary ciliary dyskinesia (PCD, cystic fibrosis (CF or protracted bacterial bronchitis (PBB can be easily missed at the general practitioner (GP.To develop and test a questionnaire-based and data mining-supported tool providing diagnostic support for selected pulmonary diseases.First, interviews with parents of affected children were conducted and analysed. These parental observations during the pre-diagnostic time formed the basis for a new questionnaire addressing the parents' view on the disease. Secondly, parents with a sick child (e.g. PCD, PBB answered the questionnaire and a data base was set up. Finally, a computer program consisting of eight different classifiers (support vector machine (SVM, artificial neural network (ANN, fuzzy rule-based, random forest, logistic regression, linear discriminant analysis, naive Bayes and nearest neighbour and an ensemble classifier was developed and trained to categorise any given new questionnaire and suggest a diagnosis. For estimating the diagnostic accuracy, we applied ten-fold stratified cross validation.All questionnaires of patients suffering from CF, asthma (AS, PCD, acute bronchitis (AB and the healthy control group were correctly diagnosed by the fusion algorithm. For the pneumonia (PM group 19/21 (90.5% and for the PBB group 17/18 (94.4% correct diagnoses could be reached. The program detected the correct diagnoses with an overall sensitivity of 98.8%. Receiver operating characteristics (ROC analyses confirmed the accuracy of this diagnostic tool. Case studies highlighted the applicability of the tool in the daily work of a GP.For children with symptoms of pulmonary diseases a questionnaire-based diagnostic support tool using data mining techniques exhibited good results in arriving at diagnostic suggestions. In the hands of a doctor, this tool could be of value in arousing awareness for

  17. Early Statin Use and the Progression of Alzheimer Disease: A Total Population-Based Case-Control Study.

    Science.gov (United States)

    Lin, Feng-Cheng; Chuang, Yun-Shiuan; Hsieh, Hui-Min; Lee, Tzu-Chi; Chiu, Kuei-Fen; Liu, Ching-Kuan; Wu, Ming-Tsang

    2015-11-01

    The protective effect of statin on Alzheimer disease (AD) is still controversial, probably due to the debate about when to start the use of statin and the lack of any large-scale randomized evidence that actually supports the hypothesis. The purpose of this study was to examine the protective effect of early statin use on mild-to-moderate AD in the total Taiwanese population.This was a total population-based case-control study, using the total population of Taiwanese citizens seen in general medical practice; therefore, the findings can be applied to the general population. The study patients were those with newly diagnosed dementia (ICD-9 290.x) and prescribed any acetylcholinesterase inhibitors (AChEI) from the Taiwan National Health Insurance dataset in 1997 to 2008. The newly diagnosed eligible mild-to-moderate AD patients were traced from the dates of their index dates, which was defined as the first day to receive any AChEI treatment, back to 1 year (exposure period) to categorize them into AD with early statin use and without early statin use. Early statin use was defined as patients using statin before AChEI treatment. Alzheimer disease patients with early statin use were those receiving any statin treatment during the exposure period. Then, we used propensity-score-matched strategy to match these 2 groups as 1:1. The matched study patients were followed-up from their index dates. The primary outcome was the discontinuation of AChEI treatment, indicating AD progression.There were 719 mild-to-moderate AD-paired patients with early statin use and without early statin use for analyses. Alzheimer disease progression was statistically lower in AD patients with early statin use than those without (P = 0.00054). After adjusting for other covariates, mild-to-moderate AD patients with early stain use exhibited a 0.85-risk (95% CI = 0.76-0.95, P = 0.0066) to have AD progression than those without.Early statin use was significantly associated with a reduction in AD

  18. Data mining in psychiatric research.

    Science.gov (United States)

    Tovar, Diego; Cornejo, Eduardo; Xanthopoulos, Petros; Guarracino, Mario R; Pardalos, Panos M

    2012-01-01

    Mathematical sciences and computational methods have found new applications in fields like medicine over the last few decades. Modern data acquisition and data analysis protocols have been of great assistance to medical researchers and clinical scientists. Especially in psychiatry, technology and science have made new computational methods available to assist the development of predictive modeling and to identify diseases more accurately. Data mining (or knowledge discovery) aims to extract information from large datasets and solve challenging tasks, like patient assessment, early mental disease diagnosis, and drug efficacy assessment. Accurate and fast data analysis methods are very important, especially when dealing with severe psychiatric diseases like schizophrenia. In this paper, we focus on computational methods related to data analysis and more specifically to data mining. Then, we discuss some related research in the field of psychiatry.

  19. Data mining concepts and techniques

    CERN Document Server

    Han, Jiawei

    2005-01-01

    Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and app...

  20. Commercial Online Social Network Data and Statin Side-Effect Surveillance: A Pilot Observational Study of Aggregate Mentions on Facebook.

    Science.gov (United States)

    Huesch, Marco D

    2017-12-01

    Surveillance of the safety of prescribed drugs after marketing approval has been secured remains fraught with complications. Formal ascertainment by providers and reporting to adverse-event registries, formal surveys by manufacturers, and mining of electronic medical records are all well-known approaches with varying degrees of difficulty, cost, and success. Novel approaches may be a useful adjunct, especially approaches that mine or sample internet-based methods such as online social networks. A novel commercial software-as-a-service data-mining product supplied by Sysomos from Datasift/Facebook was used to mine all mentions on Facebook of statins and stain-related side effects in the US in the 1-month period 9 January 2017 through 8 February 2017. A total of 4.3% of all 25,700 mentions of statins also mentioned typical stain-related side effects. Multiple methodological weaknesses stymie interpretation of this percentage, which is however not inconsistent with estimates that 5-20% of patients taking statins will experience typical side effects at some time. Future work on pharmacovigilance may be informed by this novel commercial tool, but the inability to mine the full text of a posting poses serious challenges to content categorization.

  1. Spectral Feature Selection for Data Mining

    CERN Document Server

    Zhao, Zheng Alan

    2011-01-01

    Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection. The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its th

  2. Data Mining in Institutional Economics Tasks

    Science.gov (United States)

    Kirilyuk, Igor; Kuznetsova, Anna; Senko, Oleg

    2018-02-01

    The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given.

  3. Data mining and visualization techniques

    Science.gov (United States)

    Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

    2004-03-23

    Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

  4. Open data mining for Taiwan's dengue epidemic.

    Science.gov (United States)

    Wu, ChienHsing; Kao, Shu-Chen; Shih, Chia-Hung; Kan, Meng-Hsuan

    2018-03-13

    By using a quantitative approach, this study examines the applicability of data mining technique to discover knowledge from open data related to Taiwan's dengue epidemic. We compare results when Google trend data are included or excluded. Data sources are government open data, climate data, and Google trend data. Research findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest classification power followed by climate variables (temperature and humidity), whereas gender and age show the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (respectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in the context of public health care. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. Data Mining Smart Energy Time Series

    Directory of Open Access Journals (Sweden)

    Janina POPEANGA

    2015-07-01

    Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

  6. Statin prescribing for people with severe mental illnesses: a staggered cohort study of 'real-world' impacts.

    Science.gov (United States)

    Blackburn, R; Osborn, D; Walters, K; Falcaro, M; Nazareth, I; Petersen, I

    2017-03-07

    To estimate the 'real-world effectiveness of statins for primary prevention of cardiovascular disease (CVD) and for lipid modification in people with severe mental illnesses (SMI), including schizophrenia and bipolar disorder. Series of staggered cohorts. We estimated the effect of statin prescribing on CVD outcomes using a multivariable Poisson regression model or linear regression for cholesterol outcomes. 587 general practice (GP) surgeries across the UK reporting data to The Health Improvement Network. All permanently registered GP patients aged 40-84 years between 2002 and 2012 who had a diagnosis of SMI. Exclusion criteria were pre-existing CVD, statin-contraindicating conditions or a statin prescription within the 24 months prior to the study start. One or more statin prescriptions during a 24-month 'baseline' period (vs no statin prescription during the same period). The primary outcome was combined first myocardial infarction and stroke. All-cause mortality and total cholesterol concentration were secondary outcomes. We identified 2944 statin users and 42 886 statin non-users across the staggered cohorts. Statin prescribing was not associated with significant reduction in CVD events (incident rate ratio 0.89; 95% CI 0.68 to 1.15) or all-cause mortality (0.89; 95% CI 0.78 to 1.02). Statin prescribing was, however, associated with statistically significant reductions in total cholesterol of 1.2 mmol/L (95% CI 1.1 to 1.3) for up to 2 years after adjusting for differences in baseline characteristics. On average, total cholesterol decreased from 6.3 to 4.6 in statin users and 5.4 to 5.3 mmol/L in non-users. We found that statin prescribing to people with SMI in UK primary care was effective for lipid modification but not CVD events. The latter finding may reflect insufficient power to detect a smaller effect size than that observed in randomised controlled trials of statins in people without SMI. Published by the BMJ Publishing Group Limited. For

  7. GeoDMA—Geographic Data Mining Analyst

    Science.gov (United States)

    Körting, Thales Sehn; Garcia Fonseca, Leila Maria; Câmara, Gilberto

    2013-08-01

    Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in 1 year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote database. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.

  8. Spatiotemporal Data Mining: A Computational Perspective

    Directory of Open Access Journals (Sweden)

    Shashi Shekhar

    2015-10-01

    Full Text Available Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

  9. A population-based case-control study on statin exposure and risk of acute diverticular disease.

    Science.gov (United States)

    Sköldberg, Filip; Svensson, Tobias; Olén, Ola; Hjern, Fredrik; Schmidt, Peter T; Ljung, Rickard

    2016-01-01

    A reduced risk of perforated diverticular disease among individuals with current statin exposure has been reported. The aim of the present study was to investigate whether statins reduce the risk of acute diverticular disease. A nation-wide population-based case-control study was performed, including 13,127 cases hospitalised during 2006-2010 with a first-time diagnosis of colonic diverticular disease, and 128,442 control subjects (matched for sex, age, county of residence and calendar year). Emergency surgery, assumed to be a proxy for complicated diverticulitis, was performed on 906 of the cases during the index admission, with 8818 matched controls. Statin exposure was classified as "current" or "former" if a statin prescription was last dispensed ≤ 125 days or >125 days before index date, respectively. The association between statin exposure and acute diverticular disease was investigated by conditional logistic regression, including models adjusting for country of birth, educational level, marital status, comorbidities, nonsteroidal anti-inflammatory drug/steroid exposure and healthcare utilisation. A total of 1959 cases (14.9%) and 16,456 controls (12.8%) were current statin users (crude OR 1.23 [95% CI 1.17-1.30]; fully adjusted OR 1.00 [0.94-1.06]). One hundred and thirty-two of the cases subjected to surgery (14.6%), and 1441 of the corresponding controls (16.3%) were current statin users (crude OR 0.89 [95% CI 0.73-1.08]; fully adjusted OR 0.70 [0.55-0.89]). The results do not indicate that statins affect the development of symptomatic diverticular disease in general. However, current statin use was associated with a reduced risk of emergency surgery for diverticular disease.

  10. APPLYING DATA MINING APPROACHES TO FURTHER ...

    Science.gov (United States)

    This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space. This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space.

  11. Data Mining: Going beyond Traditional Statistics

    Science.gov (United States)

    Zhao, Chun-Mei; Luan, Jing

    2006-01-01

    The authors provide an overview of data mining, giving special attention to the relationship between data mining and statistics to unravel some misunderstandings about the two techniques. (Contains 1 figure.)

  12. Contemporary rates and correlates of statin use and adherence in nondiabetic adults with cardiovascular risk factors: The KP CHAMP study.

    Science.gov (United States)

    Go, Alan S; Fan, Dongjie; Sung, Sue Hee; Inveiss, Alda I; Romo-LeTourneau, Victoria; Mallya, Usha G; Boklage, Susan; Lo, Joan C

    2017-12-01

    Statin therapy is highly efficacious in the prevention of fatal and nonfatal atherosclerotic events in persons at increased cardiovascular risk. However, its long-term effectiveness in practice depends on a high level of medication adherence by patients. We identified nondiabetic adults with cardiovascular risk factors between 2008 and 2010 within a large integrated health care delivery system in Northern California. Through 2013, we examined the use and adherence of newly initiated statin therapy based on data from dispensed prescriptions from outpatient pharmacy databases. Among 209,704 eligible adults, 68,085 (32.5%) initiated statin therapy during the follow-up period, with 90.4% receiving low-potency statins. At 12 and 24 months after initiating statins, 84.3% and 80.2%, respectively, were actively receiving statin therapy, but only 42% and 30%, respectively, had no gaps in treatment during those time periods. There was also minimal switching between statins or use of other lipid-lowering therapies for augmentation during follow-up. Age≥50 years, Asian/Pacific Islander race, Hispanic ethnicity, prior myocardial infarction, prior ischemic stroke, hypertension, and baseline low-density lipoprotein cholesterol>100 mg/dL were associated with higher adjusted odds, whereas female gender, black race, current smoking, dementia were associated with lower adjusted odds, of active statin treatment at 12 months after initiation. There remain opportunities for improving prevention in patients at risk for cardiovascular events. Our study identified certain patient subgroups that may benefit from interventions to enhance medication adherence, particularly by minimizing treatment gaps and discontinuation of statin therapy within the first year of treatment. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Genome-wide association of lipid-lowering response to statins in combined study populations.

    Directory of Open Access Journals (Sweden)

    Mathew J Barber

    2010-03-01

    Full Text Available Statins effectively lower total and plasma LDL-cholesterol, but the magnitude of decrease varies among individuals. To identify single nucleotide polymorphisms (SNPs contributing to this variation, we performed a combined analysis of genome-wide association (GWA results from three trials of statin efficacy.Bayesian and standard frequentist association analyses were performed on untreated and statin-mediated changes in LDL-cholesterol, total cholesterol, HDL-cholesterol, and triglyceride on a total of 3932 subjects using data from three studies: Cholesterol and Pharmacogenetics (40 mg/day simvastatin, 6 weeks, Pravastatin/Inflammation CRP Evaluation (40 mg/day pravastatin, 24 weeks, and Treating to New Targets (10 mg/day atorvastatin, 8 weeks. Genotype imputation was used to maximize genomic coverage and to combine information across studies. Phenotypes were normalized within each study to account for systematic differences among studies, and fixed-effects combined analysis of the combined sample were performed to detect consistent effects across studies. Two SNP associations were assessed as having posterior probability greater than 50%, indicating that they were more likely than not to be genuinely associated with statin-mediated lipid response. SNP rs8014194, located within the CLMN gene on chromosome 14, was strongly associated with statin-mediated change in total cholesterol with an 84% probability by Bayesian analysis, and a p-value exceeding conventional levels of genome-wide significance by frequentist analysis (P = 1.8 x 10(-8. This SNP was less significantly associated with change in LDL-cholesterol (posterior probability = 0.16, P = 4.0 x 10(-6. Bayesian analysis also assigned a 51% probability that rs4420638, located in APOC1 and near APOE, was associated with change in LDL-cholesterol.Using combined GWA analysis from three clinical trials involving nearly 4,000 individuals treated with simvastatin, pravastatin, or atorvastatin, we

  14. Discontinuation and restarting in patients on statin treatment: prospective open cohort study using a primary care database

    Science.gov (United States)

    Coupland, Carol; Brindle, Peter; Hippisley-Cox, Julia

    2016-01-01

    Objectives To estimate rates of discontinuation and restarting of statins, and to identify patient characteristics associated with either discontinuation or restarting. Design Prospective open cohort study. Setting 664 general practices contributing to the Clinical Practice Research Datalink in the United Kingdom. Data extracted in October 2014. Participants Incident statin users aged 25-84 years identified between January 2002 and September 2013. Patients with statin prescriptions divided into two groups: primary prevention and secondary prevention (those already diagnosed with cardiovascular disease). Patients with statin prescriptions in the 12 months before study entry were excluded. Main outcome measures Discontinuation of statin treatment (first 90 day gap after the estimated end date of a statin prescription), and restarting statin treatment for those who discontinued (defined as any subsequent prescription between discontinuation and study end). Results Of 431 023 patients prescribed statins as primary prevention with a median follow-up time of 137 weeks, 47% (n=204 622) discontinued treatment and 72% (n=147 305) of those who discontinued restarted. Of 139 314 patients prescribed statins as secondary prevention with median follow-up time of 182 weeks, 41% (n=57 791) discontinued treatment and 75% (43 211) of those who discontinued restarted. Younger patients (aged ≤50 years), older patients (≥75 years), women, and patients with chronic liver disease were more likely to discontinue statins and less likely to restart. However, patients in ethnic minority groups, current smokers, and patients with type 1 diabetes were more likely to discontinue treatment but then were more likely to restart, whereas patients with hypertension and type 2 diabetes were less likely to discontinue treatment and more likely to restart if they did discontinue. These results were mainly consistent in the primary prevention and secondary prevention groups

  15. The utility of observational studies in clinical decision making: lessons learned from statin trials.

    Science.gov (United States)

    Foody, JoAnne M; Mendys, Phillip M; Liu, Larry Z; Simpson, Ross J

    2010-05-01

    Contemporary clinical decision making is well supported by a wide variety of information sources, including clinical practice guidelines, position papers, and insights from randomized controlled trials (RCTs). Much of our fundamental understanding of cardiovascular risk factors is based on multiple observations from major epidemiologic studies, such as The Seven Country Studies and the US-based Framingham Heart Study. These studies provided the framework for the development of clinical practice guidelines, including the National Cholesterol Education Program Adult Treatment Panel series. The objective of this article is to highlight the value of observational studies as a complement to clinical trial data for clinical decision making in real-world practice. Although RCTs are still the benchmark for assessing clinical efficacy and safety of a specific therapeutic approach, they may be of limited utility to practitioners who must then adapt the lessons learned from the trial into the patient care environment. The use of well-structured observational studies can improve our understanding of the translation of clinical trials into clinical practice, as demonstrated here with the example of statins. Although such studies have their own limitations, improved techniques for design and analysis have reduced the impact of bias and confounders. The introduction of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines has provided more uniformity for such studies. When used together with RCTs, observational studies can enhance our understanding of effectiveness and utility in real-world clinical practice. In the examples of statin observational studies, the results suggest that relative effectiveness of different statins and potential impact of switching statins should be carefully considered in treating individual patients by practicing physicians.

  16. Applying data mining methods to the assessment of soil contamination and carbon sequestration under Mediterranean Climate. The case study of Guadiamar basin (SW Spain).

    Science.gov (United States)

    Muñoz Vallés, Sara; Pino-Mejías, Rafael; Blanco-Velázquez, Francisco J.; Anaya-Romero, María

    2017-04-01

    In the present background of increasing access to vast datasets of soil and environmental records, the application of the newest analytical techniques and approaches for modelling offer excellent opportunities to define recommendations and simulate processes for land degradation and management. In this regard, data mining techniques have been successfully applied in different fields of environmental sciences, performing an innovative tool to explore relevant questions and providing valuable results and useful applications through an efficient management and analysis of large and heterogeneous datasets. Soil Organic matter, pH and trace elements in soil perform close relationships, with ability to alter each other and lead to emerging, synergic properties for soils. In addition, effects associated to climate and land use change promotes mechanisms of feedback that could amplify the negative effects of soil contamination on human health, biodiversity conservation and soil ecosystem services maintenance. The aim of this study was to build and compare several data mining models for the prediction of potential and interrelated functions of soil contamination and carbon sequestration by soils. In this context, under the framework of the EU RECARE project (Preventing and Remediating degradation of Soils in Europe through Land Care), the Guadiamar valley (SW Spain) is used as case study. The area was affected by around four hm3 of acid waters and two hm3 of mud rich in heavy metals, resulting from a mine spill, in 1998, where more than 4,600 ha of agricultural and pasture land were affected. The area was subjected to a large-scale phyto-management project, and consequently protected as "Green Corridor". In this study, twenty environmental variables were taken into account and several base models for supervised classification problems were selected, including linear and quadratic discriminant analysis, logistic regression, neural networks and support vector machines. A

  17. Advanced Data Mining of Leukemia Cells Micro-Arrays

    Directory of Open Access Journals (Sweden)

    Ryan M. Pierce

    2009-12-01

    Full Text Available This paper provides continuation and extensions of previous research by Segall and Pierce (2009a that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM. As Segall and Pierce (2009a and Segall and Pierce (2009b the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is provided on the work of others pertaining to the applications of data mining to micro-array databases of Leukemia cells and micro-array databases in general. As noted in predecessor article by Segall and Pierce (2009a, micro-array databases are one of the most popular functional genomics tools in use today. This research in this paper is intended to use advanced data mining technologies for better interpretations and knowledge discovery as generated by the patterns of gene expressions of HL60, Jurkat, NB4 and U937 Leukemia cells. The advanced data mining performed entailed using other data mining tools such as cubic clustering criterion, variable importance rankings, decision trees, and more detailed examinations of data mining statistics and study of other self-organized maps (SOM clustering regions of workspace as generated by SAS Enterprise Miner version 4. Conclusions and future directions of the research are also presented.

  18. data mining in distributed database

    International Nuclear Information System (INIS)

    Ghunaim, A.A.A.

    2007-01-01

    as we march into the age of digital information, the collection and the storage of large quantities of data is increased, and the problem of data overload looms ominously ahead. it is estimated today that the volume of data stored by a company doubles every year but the amount of meaningful information is decreases rapidly. the ability to analyze and understand massive datasets lags far behind the ability to gather and store the data. the unbridled growth of data will inevitably lead to a situation in which it is increasingly difficult to access the desired information; it will always be like looking for a needle in a haystack, and where only the amount of hay will be growing all the time . so, a new generation of computational techniques and tools is required to analyze and understand the rapidly growing volumes of data . and, because the information technology (it) has become a strategic weapon in the modern life, it is needed to use a new decision support tools to be an international powerful competitor.data mining is one of these tools and its methods make it possible to extract decisive knowledge needed by an enterprise and it means that it concerned with inferring models from data , including statistical pattern recognition, applied statistics, machine learning , and neural networks. data mining is a tool for increasing productivity of people trying to build predictive models. data mining techniques have been applied successfully to several real world problem domains; but the application in the nuclear reactors field has only little attention . one of the main reasons, is the difficulty in obtaining the data sets

  19. Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

    DEFF Research Database (Denmark)

    Pinto, Tiago; Santos, Gabriel; Pereira, Ivo F.

    2014-01-01

    countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms...... to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts...

  20. Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

    Science.gov (United States)

    Al-Saggaf, Yeslam; Islam, Md Zahidul

    2015-08-01

    This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.

  1. DATA MINING IN SPORTS BETTING

    Directory of Open Access Journals (Sweden)

    Cristian Georgescu

    2013-12-01

    Full Text Available n this paper, we have made a brief analysis on how to make decisions in betting on European football with the help of data mining techniques. Whether you refer to betting a few days in advance of the sporting event or live betting, both options have been taken into consideration. By using a clustering algorithm for analyzing both the database containing events from football matches and the odds given by bookmakers, we have obtained graphs indicating the probabilities associated with analyzed events. Given the purely informative aspect of the current paper, we have only analyzed the number of corners from a match.

  2. Data mining utilizando redes neuronales

    OpenAIRE

    Ale, Juan María; Bot, Romina Laura

    2004-01-01

    Las Redes Neuronales son ampliamente utilizadas para tareas relacionadas con reconocimiento de patrones y clasificación. Aunque son clasificadores muy precisos, no son comúnmente utilizadas para Data Mining porque producen modelos de aprendizaje inexplicables. El algoritmo TREPAN extrae hipótesis explicables de una Red Neuronal entrenada. Las hipótesis producidas por el algoritmo se representan con un árbol de decisión que aproxima a la red. Los árboles de decisión extraídos por TREPAN no pue...

  3. Landslide detection using LiDAR data and data mining technology: Ali Mountain Highway case study (Taiwan)

    Science.gov (United States)

    Cheng, Youg-Sin; Yu, Teng-To; Tarolli, Paolo

    2017-04-01

    Taiwan mountains are severely affected each year by landslides, rock falls, and debris flows where the roads system suffer the most critical consequences. Among all mountain highways, Ali Highway, located into the main entrance of Alishan Mountain region, is one of the most landslide-prone areas in southern Taiwan. During the typhoon season, between May and August, the probability of occurrence of mass movements is at higher level than usual seeing great erosion rates. In fact, during Typhoon Morakot, in 2009, the intense rainfall caused abrupt interruption of the circulation for three months triggering several landslides (Liu et al. 2012). The topographic features such as slope, roughness and curvature among others have been extracted from 1 m DTM derived by a LiDAR dataset (collected in 2015) to investigate the slope failures along the Ali Mountain Highway. The high-resolution DTM highlighted that the hydrogeomorphological (e.g. density of stream, the distance from the ridge and terrain) features are one of the most influencing factors affecting the change and the instability of the slopes. To detect the landslide area, the decision tree classifier and the random forest algorithm (RF) have been adopted. The results provided a suitable analysis of the area involved in the failure. This will be a useful step in the understanding (and management) landslide processes of study area. References Liu CN, Dong JJ, Chen CJ, Lee WF (2012) Typical landslides and related mechanisms in Ali Mountain highway induced by typhoon Morakot: Perspectives from engineering geology. Landslides 9:239-254.

  4. Data Mining Tools in Science Education

    Directory of Open Access Journals (Sweden)

    Premysl Zaskodny

    2012-12-01

    Full Text Available The main principle of paper is Data Mining in Science Education (DMSE as Problem Solving. The main goal of paper is consisting in Delimitation of Complex Data Mining Tool and Partial Data Mining Tool of DMSE. The procedure of paper is consisting of Data Preprocessing in Science Education, Data Processing in Science Education, Description of Curricular Process as Complex Data Mining Tool (CP-DMSE, Description of Analytical Synthetic Modeling as Partial Data Mining Tool (ASM-DMSE and finally Application of CPDMSE and ASM-DMSE via Physics Education.

  5. Statistically significant relational data mining :

    Energy Technology Data Exchange (ETDEWEB)

    Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann; Pinar, Ali; Robinson, David Gerald; Berger-Wolf, Tanya; Bhowmick, Sanjukta; Casleton, Emily; Kaiser, Mark; Nordman, Daniel J.; Wilson, Alyson G.

    2014-02-01

    This report summarizes the work performed under the project (3z(BStatitically significant relational data mining.(3y (BThe goal of the project was to add more statistical rigor to the fairly ad hoc area of data mining on graphs. Our goal was to develop better algorithms and better ways to evaluate algorithm quality. We concetrated on algorithms for community detection, approximate pattern matching, and graph similarity measures. Approximate pattern matching involves finding an instance of a relatively small pattern, expressed with tolerance, in a large graph of data observed with uncertainty. This report gathers the abstracts and references for the eight refereed publications that have appeared as part of this work. We then archive three pieces of research that have not yet been published. The first is theoretical and experimental evidence that a popular statistical measure for comparison of community assignments favors over-resolved communities over approximations to a ground truth. The second are statistically motivated methods for measuring the quality of an approximate match of a small pattern in a large graph. The third is a new probabilistic random graph model. Statisticians favor these models for graph analysis. The new local structure graph model overcomes some of the issues with popular models such as exponential random graph models and latent variable models.

  6. Data Mining of Macromolecular Structures.

    Science.gov (United States)

    van Beusekom, Bart; Perrakis, Anastassis; Joosten, Robbie P

    2016-01-01

    The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.

  7. A Data Mining Approach to Study the Impact of the Methodology Followed in Chemistry Lab Classes on the Weight Attributed by the Students to the Lab Work on Learning and Motivation

    Science.gov (United States)

    Figueiredo, M.; Esteves, L.; Neves, J.; Vicente, H.

    2016-01-01

    This study reports the use of data mining tools in order to examine the influence of the methodology used in chemistry lab classes, on the weight attributed by the students to the lab work on learning and own motivation. The answer frequency analysis was unable to discriminate the opinions expressed by the respondents according to the type of the…

  8. Statins and polyneuropathy revisited

    DEFF Research Database (Denmark)

    Svendsen, Toke de Koning; Hansen, Peter Nørregaard; García-Rodríguez, Luis Alberto

    2017-01-01

    "); current use was further classified into long-term use (5+ years) and high or low intensity use. We used conditional logistic regression to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to examine associations between polyneuropathy and statin use. RESULTS: We included 370 validated cases...... and 7400 controls. Ever use of statins was not associated with elevated risk of polyneuropathy (OR: 1.14, 95%CI: 0.84-1.54). Similarly, we found no associations between polyneuropathy risk and current use (OR: 1.11, 95% CI: 0.79-1.53), long-term use (OR: 1.13, 95%CI: 0.66-1.92), or high intensity statin......AIM: In a previous study, we found a positive association between statin use and polyneuropathy risk. Other studies reported equivocal results. The present study aimed to confirm our findings with a design similar to our previous study, but with a larger data set. METHODS: We searched medical...

  9. Effect of statin on hepatocellular carcinoma in patients with type 2 diabetes: A nationwide nested case-control study.

    Science.gov (United States)

    Kim, Gyuri; Jang, Suk-Yong; Han, Eugene; Lee, Yong-Ho; Park, Se-Young; Nam, Chung Mo; Kang, Eun Seok

    2017-02-15

    Relationship on new statin use and the risk of hepatocellular carcinoma (HCC) in patients with incident type 2 diabetes mellitus (T2DM), who might be at the risk of developing HCC, is uncertained. A nationwide population-based nested case-control study was conducted within the National Health Insurance Service National Sample Cohort 2002-2013 in Korea. Newly prescribed statin after newly diagnosed T2DM was defined as statin use. Controls were matched to case patients on age, sex, follow-up time, and the date of diabetes diagnosis at a five-to-one ratio. Odds ratios (ORs) for associations of statin use with HCC were calculated using conditional logistic regression. After at least a 5-year HCC-free period, there were 229 incident HCC cases and 1,145 matched controls from 47,738 patients with incident diabetes. Of these 229 incident HCC cases, 27 (11.8%) were statin users, whereas 378 (33.0%) were statin users among 1,145 controls. Statin use was associated with a reduced risk of HCC development (adjusted OR [AOR]= 0.36, 95% confidence interval [CI] 0.22-0.60) after adjustment for chronic viral hepatitis, liver cirrhosis, alcoholic liver disease, previous cancer, aspirin use, insulin use, sulfonylurea use, metformin use, thiazolidinedione use, history of chronic obstructive pulmonary disease, Charlson comorbidity score, household income level, and residential area. Risk reduction was accentuated with an increase of cumulative defined daily doses (cDDD) compared with non-users (AORs 0.53, 0.36, 0.32, and 0.26 in ≤60, 60-180, 181-365, and >365cDDD, respectively; P for trend statin use before HCC diagnosis may have a beneficial inhibitory effect on HCC development in a dose-dependent manner, especially in individuals with liver disease. © 2016 UICC.

  10. [Dyslipemia in diabetics treated with statins. Results of the DYSIS study in Spian].

    Science.gov (United States)

    Millán, Jesús; Alegría, Eduardo; Guijarro, Carlos; Lozano, Jose V; Vitale, Gustavo C; González-Timón, Belén; González-Juanatey, José R

    2013-11-16

    Type 2 diabetes mellitus (DM2) is characterized by carrying a high cardiovascular risk. This situation underscores the importance of intensively treating the risk factors present in diabetic patients, notably dyslipemia. The treatment with cholesterol-lowering drugs may be especially effective to reduce the cardiovascular risk in diabetic patients. Therefore, LDL-cholesterol is a priority target in the lipid management of these patients. This study analyzes the alterations in the lipid profile of diabetic patients receiving treatment with statins, which therefore may contribute to persistent cardiovascular risk in such individuals. The DYSIS (Dyslipidemia International Study) is an international, observational trial analyzing the lipid profile of patients treated with statins and followed-up on in outpatient clinics by primary care physicians and specialists. This study is referred to the data on the diabetic patients. Of the total patients enrolled in the DYSIS, the present study included 3703 patients, 39% being diabetics. A total of 59.2% of diabetics showed LDL-C out of goal; triglyceride elevation was observed in 43.6% and 36.4% showed low HDL-C. In diabetics patients with coronary heart disease, 31% had uncontrolled levels of all three lipid parameters. The prevalence of out of goal LDL-C in diabetic patients with metabolic syndrome was close to 60%; 39.8% had low levels of HDL-C and 46,6% high levels of triglycerides. In addition, 57% of diabetic patients with obesity showed LDL-C out of control, despite statins treatment. Cardiovascular diseases remain the main cause of morbidity-mortality in patients with DM2. The results of the present study show that in diabetic patients the degree of control is very limited with regard to LDL-cholesterol. More than half of diabetic patients treated with statins had LDL-cholesterol out of goal. The level of dyslipidemia control was low, despite statins treatment. Therefore, the detection of atherogenic dyslipidemia may

  11. Cloud computing in data mining – a survey

    Directory of Open Access Journals (Sweden)

    Viktor Nekvapil

    2015-01-01

    Full Text Available Cloud computing in data mining presents promising solution for businesses willing to analyse their data with lower costs or those companies which want to utilise their “big data”. In this survey, reasons for using cloud computing solutions in data mining are studied and respective tools corresponding to these reasons are evaluated. The emphasis is laid to functionality of the tools and the integration with other applications. In total, 13 solutions were evaluated.

  12. Modelling Data Mining Dynamic Code Attributes with Scheme Definition Technique

    OpenAIRE

    Sipayung, Evasaria M; Fiarni, Cut; Tanudjaja, Randy

    2014-01-01

    Data mining is a technique used in differentdisciplines to search for significant relationships among variablesin large data sets. One of the important steps on data mining isdata preparation. On these step, we need to transform complexdata with more than one attributes into representative format fordata mining algorithm. In this study, we concentrated on thedesigning a proposed system to fetch attributes from a complexdata such as product ID. Then the proposed system willdetermine the basic ...

  13. Mathematical tools for data mining set theory, partial orders, combinatorics

    CERN Document Server

    Simovici, Dan A

    2008-01-01

    The maturing of the field of data mining has brought about an increased level of mathematical sophistication. Such disciplines like topology, combinatorics, partially ordered sets and their associated algebraic structures (lattices and Boolean algebras), and metric spaces are increasingly applied in data mining research. This book presents these mathematical foundations of data mining integrated with applications to provide the reader with a comprehensive reference. Mathematics is presented in a thorough and rigorous manner offering a detailed explanation of each topic, with applications to data mining such as frequent item sets, clustering, decision trees also being discussed. More than 400 exercises are included and they form an integral part of the material. Some of the exercises are in reality supplemental material and their solutions are included. The reader is assumed to have a knowledge of elementary analysis. Features and topics: a Study of functions and relations a Applications are provided throughou...

  14. Primary Prevention With Statins

    DEFF Research Database (Denmark)

    Mortensen, Martin B; Afzal, Shoaib; Nordestgaard, Børge G

    2015-01-01

    BACKGROUND: Guidelines recommend initiating primary prevention for atherosclerotic cardiovascular disease (ASCVD) with statins based on absolute ASCVD risk assessment. Recently, alternative trial-based and hybrid approaches were suggested for statin treatment eligibility. OBJECTIVES: This study...... the population studied, 42% were eligible for statin therapy according to the 2013 American College of Cardiology/American Heart Association (ACC/AHA) risk assessment and cholesterol treatment guidelines approach, versus 56% with the trial-based approach and 21% with the hybrid approach. Among these statin......-eligible subjects, the ASCVD event rate per 1,000 person-years was 9.8, 6.8, and 11.2, respectively. The ACC/AHA-recommended absolute risk score was well calibrated around the 7.5% 10-year ASCVD risk treatment threshold and discriminated better than the trial-based or hybrid approaches. Compared with the ACC...

  15. Data Mining Solutions for the Business Environment

    Directory of Open Access Journals (Sweden)

    Ruxandra-Stefania PETRE

    2014-02-01

    Full Text Available Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making

  16. Statin Therapy and Outcome After Ischemic Stroke: Systematic Review and Meta-Analysis of Observational Studies and Randomized Trials.

    LENUS (Irish Health Repository)

    2013-01-03

    Background-Although experimental data suggest that statin therapy may improve neurological outcome after acute cerebral ischemia, the results from clinical studies are conflicting. We performed a systematic review and meta-analysis investigating the relationship between statin therapy and outcome after ischemic stroke. METHODS: The primary analysis investigated statin therapy at stroke onset (prestroke statin use) and good functional outcome (modified Rankin score 0 to 2) and death. Secondary analyses included the following: (1) acute poststroke statin therapy (≤72 hours after stroke), and (2) thrombolysis-treated patients. RESULTS: The primary analysis included 113 148 subjects (27 studies). Among observational studies, statin treatment at stroke onset was associated with good functional outcome at 90 days (pooled odds ratio [OR], 1.41; 95% confidence interval [CI], 1.29-1.56; P<0.001), but not 1 year (OR, 1.12; 95% CI, 0.9-1.4; P=0.31), and with reduced fatality at 90 days (pooled OR, 0.71; 95% CI, 0.62-0.82; P<0.001) and 1 year (OR, 0.80; 95% CI, 0.67-0.95; P=0.01). In the single randomized controlled trial reporting 90-day functional outcome, statin treatment was associated with good outcome (OR, 1.5; 95% CI, 1.0-2.24; P=0.05). No reduction in fatality was observed on meta-analysis of data from 3 randomized controlled trials (P=0.9). In studies of thrombolysis-treated patients, an association between statins and increased fatality at 90 days was observed (pooled OR, 1.25; 95% CI, 1.02-1.52; P=0.03, 3 studies, 4339 patients). However, this association was no longer present after adjusting for age and stroke severity in the largest study (adjusted OR, 1.14; 95% CI, 0.90-1.44; 4012 patients). CONCLUSIONS: In the largest meta-analysis to date, statin therapy at stroke onset was associated with improved outcome, a finding not observed in studies restricted to thrombolysis-treated patients. Randomized trials of statin therapy in acute ischemic stroke are needed.

  17. Academic Performance: An Approach From Data Mining

    Directory of Open Access Journals (Sweden)

    David L. La Red Martinez

    2012-02-01

    Full Text Available The relatively low% of students promoted and regularized in Operating Systems Course of the LSI (Bachelor’s Degree in Information Systems of FaCENA (Faculty of Sciences and Natural Surveying - Facultad de Ciencias Exactas, Naturales y Agrimensura of UNNE (academic success, prompted this work, whose objective is to determine the variables that affect the academic performance, whereas the final status of the student according to the Res. 185/03 CD (scheme for evaluation and promotion: promoted, regular or free1. The variables considered are: status of the student, educational level of parents, secondary education, socio-economic level, and others. Data warehouse (Data Warehouses: DW and data mining (Data Mining: DM techniques were used to search pro.les of students and determine success or failure academic potential situations. Classifications through techniques of clustering according to different criteria have become. Some criteria were the following: mining of classification according to academic program, according to final status of the student, according to importance given to the study, mining of demographic clustering and Kohonen clustering according to final status of the student. Were conducted statistics of partition, detail of partitions, details of clusters, detail of fields and frequency of fields, overall quality of each process and quality detailed (precision, classification, reliability, arrays of confusion, diagrams of gain / elevation, trees, distribution of nodes, of importance of fields, correspondence tables of fields and statistics of cluster. Once certain profiles of students with low academic performance, it may address actions aimed at avoiding potential academic failures. This work aims to provide a brief description of aspects related to the data warehouse built and some processes of data mining developed on the same.

  18. Statins and physical activity in older men: the osteoporotic fractures in men study.

    Science.gov (United States)

    Lee, David S H; Markwardt, Sheila; Goeres, Leah; Lee, Christine G; Eckstrom, Elizabeth; Williams, Craig; Fu, Rongwei; Orwoll, Eric; Cawthon, Peggy M; Stefanick, Marcia L; Mackey, Dawn; Bauer, Douglas C; Nielson, Carrie M

    2014-08-01

    Muscle pain, fatigue, and weakness are common adverse effects of statin medications and may decrease physical activity in older men. To determine whether statin use is associated with physical activity, longitudinally and cross-sectionally. Men participating in the Osteoporotic Fractures in Men Study (N = 5994), a multicenter prospective cohort study of community-living men 65 years and older, enrolled between March 2000 and April 2002. Follow-up was conducted through 2009. Statin use as determined by an inventory of medications (taken within the last 30 days). In cross-sectional analyses (n = 4137), statin use categories were users and nonusers. In longitudinal analyses (n = 3039), categories were prevalent users (baseline use and throughout the study), new users (initiated use during the study), and nonusers (never used). Self-reported physical activity at baseline and 2 follow-up visits using the Physical Activity Scale for the Elderly (PASE). At the third visit, an accelerometer measured metabolic equivalents (METs [kilocalories per kilogram per hour]) and minutes of moderate activity (METs ≥3.0), vigorous activity (METs ≥6.0), and sedentary behavior (METs ≤1.5). At baseline, 989 men (24%) were users and 3148 (76%) were nonusers. The adjusted difference in baseline PASE between users and nonusers was -5.8 points (95% CI, -10.9 to -0.7 points). A total of 3039 men met the inclusion criteria for longitudinal analysis: 727 (24%) prevalent users, 845 (28%) new users, and 1467 (48%) nonusers. PASE score declined by a mean (95% CI) of 2.5 (2.0 to 3.0) points per year for nonusers and 2.8 (2.1 to 3.5) points per year for prevalent users, a nonstatistical difference (0.3 [-0.5 to 1.0] points). For new users, annual PASE score declined at a faster rate than nonusers (difference of 0.9 [95% CI, 0.1 to 1.7] points). A total of 3071 men had adequate accelerometry data, 1542 (50%) were statin users. Statin users expended less METs (0.03 [95% CI, 0.02-0.04] METs less

  19. Two non-synonymous markers in PTPN21, identified by genome-wide association study data-mining and replication, are associated with schizophrenia.

    LENUS (Irish Health Repository)

    Chen, Jingchun

    2011-09-01

    We conducted data-mining analyses of genome wide association (GWA) studies of the CATIE and MGS-GAIN datasets, and found 13 markers in the two physically linked genes, PTPN21 and EML5, showing nominally significant association with schizophrenia. Linkage disequilibrium (LD) analysis indicated that all 7 markers from PTPN21 shared high LD (r(2)>0.8), including rs2274736 and rs2401751, the two non-synonymous markers with the most significant association signals (rs2401751, P=1.10 × 10(-3) and rs2274736, P=1.21 × 10(-3)). In a meta-analysis of all 13 replication datasets with a total of 13,940 subjects, we found that the two non-synonymous markers are significantly associated with schizophrenia (rs2274736, OR=0.92, 95% CI: 0.86-0.97, P=5.45 × 10(-3) and rs2401751, OR=0.92, 95% CI: 0.86-0.97, P=5.29 × 10(-3)). One SNP (rs7147796) in EML5 is also significantly associated with the disease (OR=1.08, 95% CI: 1.02-1.14, P=6.43 × 10(-3)). These 3 markers remain significant after Bonferroni correction. Furthermore, haplotype conditioned analyses indicated that the association signals observed between rs2274736\\/rs2401751 and rs7147796 are statistically independent. Given the results that 2 non-synonymous markers in PTPN21 are associated with schizophrenia, further investigation of this locus is warranted.

  20. Negative statin-related news stories decrease statin persistence and increase myocardial infarction and cardiovascular mortality

    DEFF Research Database (Denmark)

    Nielsen, Sune Fallgaard; Nordestgaard, Børge Grønne

    2016-01-01

    AIM: We tested the hypothesis that statin-related news stories, cardiovascular disease, diabetes, statin dose, calendar year, and socio-demographic status are associated with early statin discontinuation. We also examined frequency and consequences of early statin discontinuation. METHODS...... AND RESULTS: From the entire Danish population, we studied 674 900 individuals aged 40 or older who were initiated on statin therapy in 1995-2010, and followed them until 31 December 2011. Individuals on statins increased from statin discontinuation increased from 6......% in 1995 to 18% in 2010. The odds ratios for early statin discontinuation vs. continued use were 1.09 (95% confidence interval, 1.06-1.12) for negative statin-related news stories, 1.04 (1.02-1.07) per increasing calendar year, 1.04 (1.02-1.06) per increasing defined daily dose of statin, 1.05 (1...

  1. Assessing prescriptions for statins in ambulatory diabetic patients in the United States: a national, cross-sectional study.

    Science.gov (United States)

    Segars, Larry W; Lea, Amanda R

    2008-11-01

    Diabetes mellitus affects >20 million people in the United States each year, and >4000 new cases are diagnosed daily. This study assessed the prescription of statin medications in the ambulatory setting in US diabetic patients. We used data from the 2002 through 2004 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. All ambulatory medical visits associated with a diabetes diagnosis by the International Classification of Diseases, Ninth Revision, Clinical Modification were included. Prescriptions for statin medications were determined by searching each ambulatory visit for relevant drug names (trade and generic). Demographic characteristics were assessed, including survey year, sex, age group, race, ethnicity, payment type, region of the country, and physician's specialty and degree. Analyses used sample weights to calculate national estimates. From 2002 to 2004, 10,046 (unweighted) ambulatory visits were made by diabetic patients, representing a weighted national estimate of approximately 153 million visits. A statin prescription was associated with 21.1% of all diabetic visits and 14.1% of those without a hyperlipidemia-related diagnosis. Diabetic men were more likely than diabetic women to be given a prescription for a statin (odds ratio [OR], 1.38; 95% CI, 1.09-1.73). Compared with diabetic patients treated in 2002, those treated in 2003 and 2004 were more likely to be prescribed statin therapy (2003 OR, 1.51; 95% CI, 1.02-2.24; 2004 OR, 1.48; 95% CI, 1.03-2.15). Compared with diabetic patients aged 45 to 64 years, those in younger age groups were less likely to be given a statin prescription (1-24 years OR, 0.10; 95% CI, 0.01-0.84; 25-44 years OR, 0.48; 95% CI, 0.31-0.74), and those aged 65 to 74 years were more likely to be given a statin (OR, 1.38; 95% CI, 1.01-1.90). No differences were noted for diabetic patients aged > or = 75 years. From 2002 through 2004, medical visits by diabetic patients in the United States

  2. LIFESTAT – Living with statins

    DEFF Research Database (Denmark)

    Christensen, Christa Lykke; Helge, Jørn Wulff; Krasnik, Allan

    2016-01-01

    % of the population in the Scandinavian countries are treated with statins in order to maintain good health and to avoid cardiovascular disease by counteracting high blood levels of cholesterol. The potential benefit of treatment with statins should be considered in light of evidence that statin use has prevalent....... The study investigates the biological consequences of statin treatment; determines the mechanism(s) by which statin use causes muscle and mitochondrial dysfunction; and analyzes achievement of treatment goals, people's perception of disease risk, media influence on people's risk and health perception......, and the way people manage to live with the risk (personally, socially and technologically). CONCLUSIONS THE ORIGINALITY AND SUCCESS OF LIFESTAT DEPEND ON AND DERIVE FROM ITS INTERDISCIPLINARY APPROACH, IN WHICH THE DISCIPLINES CONVERGE INTO THOROUGH AND HOLISTIC STUDY AND DESCRIBE THE IMPACT OF STATIN USE...

  3. Nonindustry-sponsored preclinical studies on statins yield greater efficacy estimates than industry-sponsored studies: a meta-analysis.

    Science.gov (United States)

    Krauth, David; Anglemyer, Andrew; Philipps, Rose; Bero, Lisa

    2014-01-01

    Industry-sponsored clinical drug studies are associated with publication of outcomes that favor the sponsor, even when controlling for potential bias in the methods used. However, the influence of sponsorship bias has not been examined in preclinical animal studies. We performed a meta-analysis of preclinical statin studies to determine whether industry sponsorship is associated with either increased effect sizes of efficacy outcomes and/or risks of bias in a cohort of published preclinical statin studies. We searched Medline (January 1966-April 2012) and identified 63 studies evaluating the effects of statins on atherosclerosis outcomes in animals. Two coders independently extracted study design criteria aimed at reducing bias, results for all relevant outcomes, sponsorship source, and investigator financial ties. The I(2) statistic was used to examine heterogeneity. We calculated the standardized mean difference (SMD) for each outcome and pooled data across studies to estimate the pooled average SMD using random effects models. In a priori subgroup analyses, we assessed statin efficacy by outcome measured, sponsorship source, presence or absence of financial conflict information, use of an optimal time window for outcome assessment, accounting for all animals, inclusion criteria, blinding, and randomization. The effect of statins was significantly larger for studies sponsored by nonindustry sources (-1.99; 95% CI -2.68, -1.31) versus studies sponsored by industry (-0.73; 95% CI -1.00, -0.47) (p valuefinancial conflict information, use of an optimal time window for outcome assessment, accounting for all animals, inclusion criteria, blinding, and randomization. Possible reasons for the differences between nonindustry- and industry-sponsored studies, such as selective reporting of outcomes, require further study.

  4. Pleiotropic and Lipid?lowering Effects of Statins in Hypertension

    OpenAIRE

    Kamberi, Lulzim Selim; Bedri Bakalli, Aurora; Muhamet Budima, Norma; Rashit Gorani, Daut; Karabulut, Ahmet Muzaffer; Talat Pallaska, Kelmend

    2012-01-01

    Background: Data on the lowering effects of statins in hypertensive patients have been mixed and highly controversial. Some studies shows reductions effects of statins in blood pressure, whereas others do not. The evidence in the literature on the effects of statins on blood pressure raises the possibility that statins may directly lower blood pressure in addition to reduce cholesterol levels?pleiotropic effects of statins. Aim of the study: The role of statins as additional treatment in pati...

  5. Chronic Statin Use and Long-Term Rates of Sepsis: A Population-Based Cohort Study.

    Science.gov (United States)

    Wang, Henry E; Griffin, Russell; Shapiro, Nathan I; Howard, George; Safford, Monika M

    2016-07-01

    "Statins" have immunomodulatory and anti-inflammatory effects and may attenuate the risk of infections. We sought to determine the association between chronic statin use and long-term rates of sepsis events. We used data from 30 183 adult (≥45 years old) community-dwelling participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. The primary exposure was statin use. The primary outcome was hospitalization or emergency department treatment for sepsis. Using Cox proportional hazards models, we determined associations between statin use and first sepsis events, adjusting for patients demographics, health behaviors, chronic medical conditions, degree of medication adherence, baseline high-sensitivity C-reactive protein (hsCRP), and propensity for statin use. Approximately one-third of participants reported statin use (n = 9475, 31.4%). During the 10-year follow-up period from 2003 to 2012, there were 1500 incident sepsis events. Statin use was not associated with rates of sepsis after multivariable adjustment for demographics, health behaviors, chronic medical conditions, medication adherence, abnormal hsCRP, and propensity for statin use, hazard ratio 0.93 (95% confidence interval: 0.81-1.06). Statin use was not similarly associated with rates of sepsis when stratified by propensity for statin use or medication adherence. In the REGARDS cohort, statin use at baseline was not associated with rates of future sepsis events. © The Author(s) 2014.

  6. Data Mining for Anomaly Detection

    Science.gov (United States)

    Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

    2013-01-01

    The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

  7. Statin Use and the Risk for Incident Diabetes Mellitus in Patients with Acute Coronary Syndrome after Percutaneous Coronary Intervention: A Population-Based Retrospective Cohort Study in Taiwan.

    Science.gov (United States)

    Lin, Zhen-Fang; Wang, Chen-Yu; Shen, Li-Jiuan; Hsiao, Fei-Yuan; Lin Wu, Fe-Lin

    2016-06-01

    The purpose of this study was to examine the association between statin use by individuals and the risk for incident diabetes mellitus in patients with acute coronary syndrome (ACS) following percutaneous coronary intervention (PCI). We conducted a retrospective cohort study of patients who were hospitalized for ACS between January 1, 2006, and December 31, 2010, and who had undergone PCI (n=30,665); the data were retrieved from the Taiwan National Health Insurance Research Database. A propensity score technique was used to establish a 1:1 matched cohort for statin users and non-statin users (n=9043 for each group). The risk for incident diabetes mellitus in statin users compared to non-statin users for patients with ACS after PCI was estimated by the multivariable Cox proportional hazards regression model. Statin use was associated with a significant increase of 27% in the risk for new-onset diabetes mellitus (adjusted hazard ratio [HR] 1.27, 95% CI 1.14 to 1.41) compared to non-statin use in the matched cohort. The matched cohort analysis indicated that almost all individual statins were associated with a statistically significant increase in the risk for new-onset diabetes mellitus compared to those without statin use. Our study indicated an association between increased risk for new-onset diabetes mellitus and statin use. Because the benefits of statins in prevention of morbidity and mortality in patients with ACS are well-established, clinical decision making should not be changed for patients with existing cardiovascular disease in whom statin therapy is recommended. Copyright © 2016 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

  8. Use of statins and the risk of acute pancreatitis: a population-based case-control study.

    Science.gov (United States)

    Kuoppala, Jaana; Pulkkinen, Jukka; Kastarinen, Helena; Kiviniemi, Vesa; Jyrkkä, Johanna; Enlund, Hannes; Happonen, Pertti; Paajanen, Hannu

    2015-10-01

    The aim of this research was to examine the association between statin use and the risk of acute pancreatitis. This register-based case-control study with incidence density sampling was based on 4376 patients hospitalized in 2008-2010 for acute pancreatitis and 19 859 randomly selected age and sex-matched controls from the adult population of Finland. The relationship between statin use from 1 January 2004 to the index date and the relative incidence rate of acute pancreatitis was modelled by conditional logistic regression. The rate ratios were adjusted for comorbidities. A total of 826 (19%) cases and 2589 (13%) controls had been exposed to statins. Statin use was associated with an increased incidence rate of acute pancreatitis (odds ratio (OR) 1.25, 95% confidence interval (CI) 1.13-1.39). This increase was seen especially during the first year of use both among current (OR 1.37, 95% CI 0.94-2.00 for at most 3 months of use and OR 1.32, 95% CI 1.07-1.63 for 4-12 months of use) and former users (OR 1.64, 95% CI 1.33-2.03). The overall association remained when restricting analyses to participants with current use only, or with no history of gallstone or alcohol-related diseases, or with no comorbidities or medicines other than statins. Statin use seems to be associated with an increased risk of acute pancreatitis. The association is more apparent during the first year of statin use and among former users. Copyright © 2015 John Wiley & Sons, Ltd.

  9. Collective impact of conventional cardiovascular risk factors and coronary calcium score on clinical outcomes with or without statin therapy: The St Francis Heart Study.

    Science.gov (United States)

    Waheed, Salman; Pollack, Simcha; Roth, Marguerite; Reichek, Nathaniel; Guerci, Alan; Cao, Jie J

    2016-12-01

    The efficacy of statin therapy remains unknown in patients eligible for statin therapy with and without elevated coronary calcium score (CAC). The study sought to evaluate how cardiovascular risk factors, expressed in terms of statin eligibility for primary prevention, and CAC modify clinical outcomes with and without statin therapy. We conducted a post-hoc analysis of the St. Francis Heart Study treatment trial, a double-blind, placebo-controlled randomized controlled trial of atorvastatin (20 mg), vitamin C (1 g), and vitamin E (1000 U) daily, versus placebos in 990 asymptomatic individuals with CAC ≥ 80th percentile for age and gender. Primary cardiovascular outcomes included non-fatal myocardial infarction or coronary death, coronary revascularization, stroke, and peripheral arterial revascularization. We further stratified the treatment and placebo groups by eligibility (eligible when statin indicated) for statin therapy based on 2013 ACC/AHA guidelines and based on CAC categories. After a median follow-up of 4.8 years, cardiovascular events had occurred in 3.9% of the statin treated but not eligible, 4.6% of the untreated and not eligible, 8.9% of the treated and eligible and 13.4% of the untreated and eligible groups, respectively (p300) occurred frequently in more than 35% of the statin not eligible subjects and was associated with a high 10-year event rate (≥17 per 100 person-years). Risk prediction improved significantly when both clinical risk profile and CAC score were combined (net reclassification index p = 0.002). Under the current statin treatment guidelines a small number of statin eligible subjects with low CAC might not benefit from statin therapy within 5 years. However, the statin not eligible subjects with high CAC have high event rate attributing to loss of opportunity for effective primary prevention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. The Hazards of Data Mining in Healthcare.

    Science.gov (United States)

    Househ, Mowafa; Aldosari, Bakheet

    2017-01-01

    From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.

  11. Data mining for prospective early detection of safety signals in the Vaccine Adverse Event Reporting System (VAERS): a case study of febrile seizures after a 2010-2011 seasonal influenza virus vaccine.

    Science.gov (United States)

    Martin, David; Menschik, David; Bryant-Genevier, Marthe; Ball, Robert

    2013-07-01

    December 2010, US VAERS analyses we found an EB05 >2 for Fluzone(®) 2010-2011 and the Medical Dictionary for Regulatory Activities (MedDRA(®)) term "febrile seizure". MedDRA(®) terminology is the medical terminology developed under the auspices of the International Conference on Harmonization of technical requirements for Registration of Pharmaceuticals for Human Use (ICH). No other vaccine products had independent vaccine-febrile seizure combinations with an EB05 >2. Three-dimensional analyses to examine possible interactions among vaccine products concomitantly administered with Fluzone(®) 2010-2011 yielded Interaction Signal Score values masked vaccine adverse event pair with an EB05 >2. The inactivated vaccine database restriction resulted in a 41 % reduction in background VAERS reports and a 24 % reduction in foreground VAERS reports. Empirical Bayesian data mining in VAERS prospectively detected the safety signal for febrile seizures after Fluzone(®) 2010-2011 in young children. The EB05 threshold, database restrictions, adjustment and baseline data mining were strategies adopted a priori to enhance the specificity of the 2010-2011 influenza vaccine data mining analyses. A database restriction used to separate live vaccines resulted in a reduced EB05. Adjustment of data mining analyses had a larger effect on estimates of disproportionality than the MGPS algorithm. Masking did not appear to influence our findings. This case study illustrates the value of VAERS data mining for vaccine safety monitoring.

  12. ADMIRE framework for data mining and integration

    Science.gov (United States)

    Hluchy, Ladislav; Tran, Viet; Habala, Ondrej

    2010-05-01

    In this paper we presents the data mining and integration of environmental applications in EU IST project ADMIRE. It briefly presents the project ADMIRE and data mining of spatio-temporal data in general. The application, originally targeting flood simulation and prediction is now being extended into the broader context of environmental studies. We describe several interesting scenarios, in which data mining and integration of distributed environmental data can improve our knowledge of the relations between various hydro-meteorological variables. The project ADMIRE aims to deliver a consistent and easy-to-use technology for extracting information and knowledge. Its main target is to provide advanced data mining and integration techniques for distributed environment. In this paper, we will focus on one of its pilot applications with target domain is environmental risk management. Several scenarios have been proposed including short-term weather forecasting using radar images, complex hydrological scenarios with waterworks, measured data from water stations and meteorological data from models. Historical data for mining are supplied mainly by Slovak Hydrometeorological Institute and Slovak Water Enterprise. The main characteristics of data sets describing phenomena from environment applications are spatial and temporal dimensions. Integration of spatio-temporal data from different sources is a challenging task due to those dimensions. Different spatio-temporal data sets contain data at different resolutions and frequencies. This heterogeneity is the principal challenge of geo-spatial and temporal data sets integration - the integrated data set should hold homogeneous data of the same resolution and frequency. In alignment with ADMIRE project vision, the processing elements (a data integration workflow that can be executed at a single resource.) are specified in Data Mining and Integration Language DISPEL that is being developed within the project. The goal of DISPEL

  13. Combining statins with tissue plasminogen activator treatment after experimental and human stroke: a safety study on hemorrhagic transformation.

    Science.gov (United States)

    Campos, Mireia; García-Bonilla, Lidia; Hernández-Guillamon, Mar; Barceló, Verónica; Morancho, Anna; Quintana, Manolo; Rubiera, Marta; Rosell, Anna; Montaner, Joan

    2013-11-01

    Statins may afford neuroprotection against ischemic injury, but it remains controversial whether combined treatment with tissue plasminogen activator (tPA) after stroke increases the risk of hemorrhagic transformation (HT), the major tPA-related complication. We evaluated the safety of combining statin with tPA administration during the acute phase of both experimental and human stroke. The occurrence and severity of HT, infarct volume, and neurological outcome were evaluated in spontaneous hypertensive rats (SHR) subjected to embolic middle cerebral arterial occlusion (MCAO), which received vehicle or simvastatin (20 mg/kg), 15 min after ischemia and tPA (9 mg/kg) 3 h after ischemia. Additionally, HT rate was evaluated in stroke patients who were treated with tPA (0.9 mg/kg) within 3 h after symptom onset, considering whether or not were under statins treatment when the stroke occurred. In the experimental study, no differences in HT rates and severity were found between treatment groups, neither regarding mortality, neurological deficit, infarct volume, or metalloproteinases (MMPs) brain content. In the clinical study, HT rates and hemorrhage type were similar in stroke patients who were or not under statins treatment. This study consistently confirms that the use of statins does not increase HT rates and severity when is combined with tPA administration. © 2013 John Wiley & Sons Ltd.

  14. Use of statins and risk of glioma

    DEFF Research Database (Denmark)

    Gaist, David; Andersen, L; Hallas, Jesper

    2013-01-01

    Laboratory studies and a single case-control study have suggested a protective effect of statins on the risk of glioma. We wished to investigate the influence of statin use on the risk of glioma in a population-based setting.......Laboratory studies and a single case-control study have suggested a protective effect of statins on the risk of glioma. We wished to investigate the influence of statin use on the risk of glioma in a population-based setting....

  15. Switching statins in Norway after new reimbursement policy: a nationwide prescription study.

    Science.gov (United States)

    Sakshaug, Solveig; Furu, Kari; Karlstad, Øystein; Rønning, Marit; Skurtveit, Svetlana

    2007-10-01

    To assess the changes in prescribing of statins in Norway after implementation of the new reimbursement regulations for statins in June 2005. Data were retrieved from the Norwegian Prescription Database covering the total population in Norway (4.6 million). Outcome measures were the proportion of atorvastatin users switching to simvastatin and changes in the proportion of new statin users receiving simvastatin. Based on retail costs for all statin prescriptions dispensed in Norway, expenditure was measured in Norwegian currency. One-year prevalences of statin use increased from 6.3 to 6.8% for women and from 7.5 to 8.1% for men from the year before to the year after the new statin regulations. Of atorvastatin users (N = 131,222), 39% switched to simvastatin during the 13-month period after the implementation. The proportion of switching was higher in women (41%) than in men (36%). In May 2005, 48% of the new statin users received simvastatin. The proportion of new users receiving simvastatin increased rapidly after implementation of the new regulations to 68% in June 2005 and reached 92% in June 2006. Expenditure was reduced from 120 million to 95 million Euro when comparing the year before with the year after the new statin regulations. The new reimbursement policy for statins has had a great impact on physicians' prescribing of statins in Norway. Physicians in Norway acknowledge the importance of contributing to cost containment.

  16. A survey of temporal data mining

    Indian Academy of Sciences (India)

    Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining. We mainly concentrate on algorithms for pattern discovery in ...

  17. Collaborative Data Mining Tool for Education

    Science.gov (United States)

    Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; Gea, Miguel; de Castro, Carlos

    2009-01-01

    This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its…

  18. Target discovery from data mining approaches.

    Science.gov (United States)

    Yang, Yongliang; Adelstein, S James; Kassis, Amin I

    2012-02-01

    Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced. Published by Elsevier Ltd.

  19. [WEB-based medical data mining integration].

    Science.gov (United States)

    Yao, Gang; Zhang, Xiaoxiang; Wang, Huoming

    2014-06-01

    An integration of medical data management system based on WEB and data mining tool is reportedly in this paper. In the application process of this system, web-based medical data mining user sends requests to the server by using client browser with http protocol, the commands are then received by the server and the server calls the data mining tools remote object for data processing, and the results are sent back to the customer browser through the http protocol and presented to the user. In order to prove the feasibility of the proposed solution, the test is done under the NET platform by using SAS and SPSS, and the detail steps are given. By the practical test, it was proved that the web-based data mining tool integration solutions proposed in this paper would have its broad prospects for development, which would open up a new route to the development of medical data mining.

  20. Anti inflammatory effects of statin in COPD

    OpenAIRE

    Nasef Abdelsalam Rezk; Ahmad Elewa

    2013-01-01

    Introduction: Statins are now becoming recognized as powerful antiinflammatory agents that exert beneficial effects beyond low-density lipoprotein cholesterol reduction [1]. COPD patients receiving statins obtain a benefit from these therapeutic agents. Clearly, the best medical evidence for the association of statins with improved outcomes for COPD patients [2]. We aimed in this study to assess anti inflammatory effects of statin in COPD patients. Patients and methods: We studied 28...

  1. Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

    Directory of Open Access Journals (Sweden)

    Al Quhtani Masoud

    2017-03-01

    Full Text Available Background: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

  2. EXTRACTING KNOWLEDGE FROM DATA - DATA MINING

    Directory of Open Access Journals (Sweden)

    DIANA ELENA CODREANU

    2011-04-01

    Full Text Available Managers of economic organizations have at their disposal a large volume of information and practically facing an avalanche of information, but they can not operate studying reports containing detailed data volumes without a correlation because of the good an organization may be decided in fractions of time. Thus, to take the best and effective decisions in real time, managers need to have the correct information is presented quickly, in a synthetic way, but relevant to allow for predictions and analysis.This paper wants to highlight the solutions to extract knowledge from data, namely data mining. With this technology not only has to verify some hypotheses, but aims at discovering new knowledge, so that economic organization to cope with fierce competition in the market.

  3. Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations.

    Science.gov (United States)

    Robson, Barry; Boray, Srinidhi

    2016-06-01

    Extracting medical knowledge by structured data mining of many medical records and from unstructured data mining of natural language source text on the Internet will become increasingly important for clinical decision support. Output from these sources can be transformed into large numbers of elements of knowledge in a Knowledge Representation Store (KRS), here using the notation and to some extent the algebraic principles of the Q-UEL Web-based universal exchange and inference language described previously, rooted in Dirac notation from quantum mechanics and linguistic theory. In a KRS, semantic structures or statements about the world of interest to medicine are analogous to natural language sentences seen as formed from noun phrases separated by verbs, prepositions and other descriptions of relationships. A convenient method of testing and better curating these elements of knowledge is by having the computer use them to take the test of a multiple choice medical licensing examination. It is a venture which perhaps tells us almost as much about the reasoning of students and examiners as it does about the requirements for Artificial Intelligence as employed in clinical decision making. It emphasizes the role of context and of contextual probabilities as opposed to the more familiar intrinsic probabilities, and of a preliminary form of logic that we call presyllogistic reasoning. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Patofysiologiske aspekter ved statiners diabetogene effekt

    DEFF Research Database (Denmark)

    Pilemann-Lyberg, Sascha; Solis, Anette Bratt; Gæde, Peter

    2014-01-01

    Statins are potent inhibitors of cholesterol biosynthesis. Statins are beneficial in the primary and secondary prevention of coronary heart disease. Recent studies indicate that there is an association between statin use and the development of new-onset diabetes mellitus. This article reviews the...

  5. The multiple zeta value data mine

    International Nuclear Information System (INIS)

    Buemlein, J.; Broadhurst, D.J.

    2009-07-01

    We provide a data mine of proven results for multiple zeta values (MZVs) of the form ζ(s 1 ,s 2 ,..,s k ) = sum ∞ n 1 >n 2 >...>n k >0 {1/(n 1 s 1 ..n k s k )} with weight w = sum K i=1 s i and depth k and for Euler sums of the form sum ∞ n 1 >n 2 >...>n k >0 {(ε 1 n 1 ..ε 1 n k )/(n 1 s 1 ..n k s k )} with signs ε i = ± 1. Notably, we achieve explicit proven reductions of all MZVs with weights w≤22, and all Euler sums with weights w≤12, to bases whose dimensions, bigraded by weight and depth, have sizes in precise agreement with the Broadhurst. Kreimer and Broadhurst conjectures. Moreover, we lend further support to these conjectures by studying even greater weights (w≤30), using modular arithmetic. To obtain these results we derive a new type of relation for Euler sums, the Generalized Doubling Relations. We elucidate the ''pushdown'' mechanism, whereby the ornate enumeration of primitive MZVs, by weight and depth, is reconciled with the far simpler enumeration of primitive Euler sums. There is some evidence that this pushdown mechanism finds its origin in doubling relations. We hope that our data mine, obtained by exploiting the unique power of the computer algebra language FORM, will enable the study of many more such consequences of the double-shuffle algebra of MZVs, and their Euler cousins, which are already the subject of keen interest, to practitioners of quantum field theory, and to mathematicians alike. (orig.)

  6. Educational data mining and learning analytics

    OpenAIRE

    Vera Hernández, Joan Carles

    2017-01-01

    Treball basat en Educational Data Mining & Learning Analitics d'anàlisi de la matriculació dels alumnes i el seu impacte sobre la decisió de tornar-se a matricular. Trabajo basado en Educational Data Mining & Learning Analytics análisis de la matriculación de los alumnos y su impacto sobre la decisión de volverse a matricular. Work based on Educational Data Mining & Learning Analytics analysis of student enrollment and its impact on the decision to re-enroll.

  7. Data Mining and Statistics for Decision Making

    CERN Document Server

    Tufféry, Stéphane

    2011-01-01

    Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized lin

  8. Data Mining Tools for Malware Detection

    CERN Document Server

    Masud, Mehedy; Thuraisingham, Bhavani; Andreasson, Kim J

    2011-01-01

    Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets. The authors describe the systems they have designed and devel

  9. INTEGRATING DATA MINING INTO BUSINESS INTELLIGENCE

    Directory of Open Access Journals (Sweden)

    Maria Cristina ENACHE

    2006-01-01

    Full Text Available Data Mining is a broad term often used to describe the process of using database technology, modeling techniques, statistical analysis, and machine learning to analyze large amounts of data in an automated fashion to discover hidden patterns and predictive information in the data. By building highly complex and sophisticated statistical and mathematical models, organizations can gain new insight into their activities. The purpose of this document is to provide users with a background of a few key data mining concepts and business intelligence and about benefits of integrating business intelligence and data mining.

  10. Persistent lipid abnormalities in statin-treated patients with diabetes mellitus in Europe and Canada: results of the Dyslipidaemia International Study

    NARCIS (Netherlands)

    Leiter, L. A.; Lundman, P.; da Silva, P. M.; Drexel, H.; Jünger, C.; Gitt, A. K.; Absenger, Guun; Albrich, Ernst; Allinger, Berndt; Allinger, Stephan; Anacher, Gerald; Angermayr, Gertraud; Angermeier, Hermann; Anzengruber, Aneas; Archimanitis, Gabriele; Arnsteiner, Patricia; Auberger, Wolfgang; Azhary, Mawaheb; Barfuss, Michael; Bauer, Christian; Bauer, Birgit Elisabeth; Beclin, Thomas; Binder, Thomas; Binder, Gabriele; Böhler, Dietmar; Brändle, Johann; Breslmair, Jörg; Brettlecker, Marlis; Bürger, Michael; Calvi, Inge; Dorfinger, Werner; Doringer-Schnepf, Elisabeth; Eer, Anton; Eckmayr, Christine; Eder, Franz; Egermann, Margit; Erath, Michael; Etzinger, Michael; Etzinger, Claudia; Fiedler, Lothar; Filip, Wolfgang; Filip, Michaela; Föchterle, Johann; Fodor, Anita; Frieden, Thomas; Gareiss, Mertens; Gföllner, Peter; Ghamarian, Thomas; Goritschan, Michael; Haar, Klaus; Habeler, Gerhard; Hadjiivanov, Valery; Haiböck, Christian; Hammer, Regina; Hartmann, Siegfried; Haschkovitz, Herbert; Hauer, Walter; Hauer, Josef; Haunschmidt, Christian; Heimayr, Christine; Hengl, Wolfgang; Hengl, Gunter; Hermann, Rudolf; Herrmann, Rainer; Hillebrand, Roswitha; Hintersteininger, Otto; Hirsch, Michael; Hitzinger, Martin; Hochegger, Tanja; Hockl, Wolfgang; Hoi, Michael; Hörmann, Jan; Hudler, Brigitte; Imb, Gerhard; Joichl, Anea; Jungbauer, Karl; Kapl, Gerlinde; Kerschbaum, Margit; Kienesberger, Franz; Killinger, Gerhard; Kitzler, Gerhard; Klein, Franz; Kleinbichler, Dietmar; Kohr, Anton; Kopetzky, Michael; Korthals, Christian; Kortschak, Werner; Koschutnik, Martin; Kraus, Werner A.; Kurzemann, Susanne; Lavicka, Claus; Lehner, Guido; Lenz, Jürgen; Lepuschütz, Sabine; Lichtenwallner, Michael; Lober, Reinhard; Loidl, Christine; Lopatka, Eduard; Ludwig, Rudolf; Maca, Thomas; Mair, Anneliese; Mandak, Michael; Margreiter, Maria; Margreiter, Anea; Markovics, Michael; Matejicek, Frieich; Mohilla, Maximillian; Moll, Christian; Mörz, Beate; Mörz, Reinhard; Nagl, Heinz; Neumayr, Günther; Oberroitmair, Helmut; Oberzinner, Michael; Pallamar, Walter; Pangratz, Sibylle; Parandian, Laurenz; Paulus, Alexana; Pfaffenwimmer, Christoph; Plaichinger, Peter; Pokorn, Thomas; Polanec, Helmuth; Pöll-Weiss, Barbara; Pralea, Doralina; Puttinger, Johann; Quinton, Thomas; Ranegger, Matthias; Rass, Sepp; Rauch, Heribert; Riehs, Manfred; Robetin, Erich; Rohringer, Jörg; Rupprechter, Josef; Sadjed, Eduard; Schimbach, Johann Alois; Schmid, Jutta; Schneiderbauer, Rotraud; Schopper, Wolfgang; Schulze-Bauer, Alfred; Schuster, Gottfried; Schwarz, Johann; Schwarz, Maria; Schweighofer, Christoph; Schwelle, Franz; Simma, Hanspeter; Sock, Renate; Sock, Reinhard; Sprenger, Fritz; Stiglmayr, Thomas; Stocker, Ilse; Stütz, Pia; Tama, Mustafa; Teleky, Ursula; Tschauko, Werner; Veits, Martin; Vikydal, Gerhard; Vlaschitz, Karl; Wais, Elisabeth; Wais, Adam; Wegmann, Robert; Wehle, Franz; Weindl, Manfred; Weinhandl, Manuela; Wendt, Ursula; Wendt, Klaus; Werner-Tutschku, Volker; Werner-Tutschku, Christine; Wilscher, Josef; Wind, Norbert; Winter, Aneas; Wolfschütz, Gerald; Wolfsgruber, Markus; Wolfsgruber, Brigitte; Wurm, Renate; Ziebart-Schroth, Arno; Zimmermann, Maximillian; Zinnagl, Aneas; Zirm, Anea; Zirm Canada, Bernhard; Bokenfohr, Grace Mary; Liu, Edmond K. H.; Melling, Gordon W.; Papp, Edward William; Sachdeva, Ashok K.; Snyman, Ernst Retief; Varma, Sonya; Ward, Richard A.; Tiong Wong, Anew Pak; Basson, Paul J.; Brodie, Brian D.; Chahal, Sukhjiwan Jeevyn; Chan, William Y.; Chow, John C.; Cormack, Maura; Eddy, Donald H. J.; Ezekiel, Daniel; Farquhar, Anew; Gu, Shian; Hii, Ting H.; Ho-Asjoe, Marianne P. K.; Hosie, Anew; Jaffer, Shahin; Jakubowski, Anew T.; Karim, Mandy; Kiai, Cristina; Kooy, Jacobus; Lytle, Craig R.; Mcleod, Kevin Lain; Morgan, David C.; Myckatyn, Michael M.; Ng, John P. Y.; Schriemer, Ronnald; Schumacher, Gerhard; Grey Stopforth, James; Hoo Tsui, Winston Wai; Wilson, Robin T.; Wong, Danny; Wong, Wilfred T.; Yeung, Margaret M. W.; Cram, David Harvey; Kumari Dissanayake, Dilani Tamara; Gerber, Johan Daniel W.; Haligowski, David; Hrabarchuk, Blair; Kroczak, Tadeusz J.; Lipson, Alan H.; Mahay, Raj K.; Wessels Mare, Abraham Carel; Mohamdee, Feisal John; Olynyk, Frederick Theodore; Pieterse, Wickus; Ramgoolam, Rajenanath; Rothova, Anna; Saunders, Kevin Kenneth; Szajkowski, Stanley; van Gend, Richard F.; van Rensburg, Nicolaas Marthinus Jansen; Anand, Sanjiv; Baer, Carolyn E. H.; Basque, Eric J. Y.; Benaya, Sebastian; Bessoudo, Ricardo; Bhalla, Jaswinder; Chettiar, Nataraj V.; Craig, Brian N.; Desrosiers, France; Ranjani Imbulgoda, Manel; Morgan, Gareth M.; Nowak, Zbigniew J.; Scott, Daniel G.; Searles, Gregory R.; Slorach, J. Ninian; Stevenson, Robert N.; Browne, Noel John; Bruff, Karl Joseph; Collingwood, John Maurice; Collins, Wayne; Over, Aidan; Gabriel, Anthony M.; Govender, Moonsamy; Hart, David G.; Hatcher, Lydia B.; Janes, John; Kielty, John F.; Krisdaphongs, Michoke; Lush, Richard Boyd; Moulton, William Bertram; Riche, Cyril R.; Rideout, Gary M.; Roberts, Bernard C.; Walsh, Paul E.; Wight, Harold G.; Woodland, K. Heather; Woodland, Robert C.; Atkinson, Bradley Charles; Chow, Carlyle S. H. A.; Collins, James A.; Graham, Robert D.; Hosein, Jalal; Machel, Teresa M.; Mahaney, Gordon Ralston; Mclean, James Robert Bruce; Murray, Michael R.; Myatt, Gregory Alexander; Ozere, Christopher P.; Saha, Amal Krishna; Sanders, David Herbert; Seaman, Donald Maxwell; Seaman, James Gordon; Swinamer, Deanna; Voon Yee, Kenny Yew; Ali, Mohamed Mustapha; Bankay, Clarence D. C.; Beduhn, Eitel Erich Reinhold; Callaghan, Denis J.; Chan, Yun Kai; Chaudhri, Arif R.; Chen, Richard Y. Y.; Conway, James Robin; Cunningham, William L.; Cusimano, Steven Lawrence; Souza, Eleanor De; de Souza, Selwyn X.; Deyoung, John Paul; Epstein, Ralph; Faiers, Alan Arthur; Figurado, Victor John; Forbes, F. Basil Trayer; Gabor, Zsuzsanna; Gallardo, Rodolfo Canonizado; Gaur, Shiva K.; George, Elizabeth; Hartford, Brian J.; Shiu-Chung Ho, Michael; Ho, Chung; Ismail, Shiraz H.; Bhushan Kalra, Bharat; Koprowicz, Kinga; Kumar, Naresh; Lam, Clement; Lau, Ming-Jarm; Law, Hugo Kwok Cheung; Fung, Max Leung Sui; Liutkus, Joanne Frances; Lotfallah, Talaat K.; Luton, Robert G.; Meneses, Gloria S.; Miller, Mark Lee; Nagji, Noorbegum; Ng, Ken H. M.; Ng Thow Hing, Roland E.; Pandey, Amritanshu Shekhar; Petrov, Ivan; Rosenthall, Wendy; Rudner, Howard; Russell, Alan Douglas; Sanchez, Zenia A.; Shaban, Joseph A.; Shariff, Shiraz B. K.; Shih, Chung Ming; Sinclair, Duncan W.; Spink, Donald Richard; Tung, Tommy Hak Tsun; Vizel, Saul; Yanover, David Frederick; Zavodni, Louis S.; Cusack, Paul; Dewar, Charles M.; Hooley, Peter; Kassner, Rachel Anne; Mackinnon, Randy James; Molyneaux, Harold W.; Shetty, Karunakara Naduhithlu; Barrière, Ginette; Berjat, Maria B.; Bernucci, Bruno; Bérubé, Claude; Boueau, Ghyslain; Chehayeb, Raja; Ciricillo, Domenico; Constance, Christian M.; Côté, Gilles; Desroches, Jacques; Gagnon, Robert; Gaueau, Gilles; Godbout, Jean Louis; Harvey, Pierre; Hassan, Youssef; Hoang, Ngoc Vinh; Houde, Danielle; Lalonde, Alain-Paul; Lavoie, Régis; Leclair, Normand; Meagher, Luc; Ouimet, Alain; Plourde, Simon; Rioux, Denis W.; Roberge, Claude; Roy, Bruno; Sasseville, Richard; Serfaty, Samuel; Theriault, Lyne; Timothée, Jean R.; Tjia, Sabine; Tremblay, Bruno; Turcotte, Jean; Bose, Sabyasachi; Aletta Bouwer, Hester; Chernesky, Patricia A.; Johnson, Mervin Louis; Kemp, David R.; Lai, Raymond Pong-Che; Lee, Frank R.; Lipsett, William G. C.; Lombard, Schalk J.; Majid, Falah S.; Malan, Johannes J.; Maree, Narinda; Nayar, Arun; Nel, Mandi; Oduntan, Oluwole O.; Rajakumar, Alphonsus R. J.; Baraka Ramadan, Fauzi; Shamsuzzaman, Mohammed; Vermeulen, Abraham P. M.; Fred, C.; Anthonsen, Birgitte; Ardest, Steen Pennerup; Arnold-Larsen, Susanne Kajsa; Axelsen, Allan; Barfoed, Klaus; Birkler, Niels Erik; Blokkebak, Jens; Boserup, Jørgen; Kettrup Brassøe, Jens Ole; Chovanec, Martin; Lykke Christensen, Bendt; Christensen, Micael; Skjøth Christensen, Randi; Eidner, Per Olav; Eisbo, Jørn; Elsvor, Jan; Engmann, Ida Veng-Christensen; Eriksen, Rene Milling; Frederiksen, Thorkil; Frølund, Hanne Charlotte; Garne, Susanne; Giørtz, Agnete; Gregersen, Bettina; Halkier, Merete Lundbye; Hansen, Jens Georg; Harder, Jan; Jørgen, Hans; Henriksen, O.; Kirkeby Hoffmann, Michael; Holk, Erik; Hollensen, Jan; Jacobsen, Rune; Jakobsen, Lotte; Jensen, Christian; Jensen, Morten; Jensen, Vibeke; Jepsen, Peter; Johannsen, Jens Arne; Verner Johansen, Lars; Johansen, Ole Steen; Juul, Kristian; Jørgensen, Arvid Frank; Jørgensen, Peter; Jørgensen, Ulrik Miilmann; Kensmark, Lars; Kjellerup, Carsten; Kjaer, Ejner; Kjaersgaard, Morten; Klubien, Peter; Kolby, Peter; Korsgaard Thomsen, Kristian; Krebs, Peter; Kristiansen, Tom; Lyng, Flemming; Madsen, Natalia V.; Meyer-Christensen, Jesper; Mogensen, Ole; Mortensen, Finn; Nielsen, Lotta Marie; Nielsen, Per Schiwe; Nielsen, Søren Kjærem; Ommen, Henrik; Juhl Otte, Jens; Østergaard Paridon, Volle; Parm, Michael; Peampour, Kian; Petersen, Kirsten; Pilgaard, Peder Jensen; Poulsen, Svend Erik; Preisler, Thomas; Hast Prins, Søren Ulrik; Randløv, Annette; Rasmussen, Birgit Reindahl; Elmegaard Rasmussen, Peter; Rasmussen, Regnar; Roed, Søren Flemming; Sander, Kirsten Foltmar; Schmidt, Ejnar Ørum; Jørgen Schultz, Paul; Smidemann, Margit; Solgaard, Jørgen; Stripp, Tommy; Søderlund, Michael Rene M.; Søgaard, Henning; Søndergaard, Dorte E.; Sørensen, Birgitte H.; Sørensen, Gerhard Seth; Thøgersen, Niels; Toftdahl, Hans; Uggerhøj, Hanne; Uhrenholt, Bjarne; Veronika Ullisch, Eva; Valentiner-Branth, Christian; Vinberg, Jørgen; Vinter, Svend Aage; Vittrup, Preben; Winther-Pedersen, Niels; Wøldike, Anne Grete; Zederkof, Jørgen M.; Thue Østergaard, Merete; Abiven, Patrick; Abraham, Dominique; de Beaumais, Philippe Adam; Ado, Jean Pierre; Affres, Helene; Agache, Regis; Airault Leman, Anne Marie; Moussarih, Abdallah Al; Albaric, Christian; Allaouchiche, Thierry; Allignol, Christian; Ammor, Mohammed; Ammoun Bourdelas, Corinne; Amsallem, Luc; Anquez, Denis; Antonini, Jean Michel; Assuied, Virginia; Attia, Gerard; Audebert, Olivier; Audibert, Henri; Ayach, Claude; Bagdadlian, Serge; Bagni, Marina; Baillet, Jean; Ballivian Cardozo, Fernando; Baranes, Robert; Barbier, Patricia; Barousse, Francoise; Bas, Sylvie; Battaglia, Jean Marc; Baudonnat, Bruno; Bauple, Jean Louis; Domengetroy, Frederic Baylac; Beard, Thierry; Beaumier, Eric; Beaumont, Jean Francois; Baylac Domengetroy, Frederic; Beck, Christian; Behar, Michel; Behr, Bernard; Benady, Richard; Benghanem, Mohamed Mounir; Benichou, Herve; Bensoussan, Jean Marc; Bensussan, Pierre; Bercegeay, Pascal; Berneau, Jean Baptiste; Bertolotti, Alexane; Bertrand, Sylviane; Besson, Alain; Bezanson, Christophe; Bezier, Christophe; Bezzina, Remy; Bichon, Herve; Bickar, Pierre; Billot, Pierre; Billot Belmere, Marie Claude; Bisson, Francois; Blanc, Dominique; Bloch, Jean Luc; Bloch, Bernard; Blondin, Hyacinthe; Blot, Jacques; Bloud, Raymond; Blouin, Pascal; Boesch, Christophe; Boiteux, Jean Luc; Bonnafous, Pierre; Bonneau, Yanick; Bonnefoy, Laurent; Borg, Bernard; Borys, Jean Michel; Brunehaut Petaut, Myriam; Boschmans, Sabine; Said, Rami Bou; Bouallouche, Abderrahmane; Bouchet, Jacques; Bouchlaghem, Khaled; Boulen, Yvon; Bouline, Benoit; Bounekhla, Mohamed Salah; Bouquin, Vincent; Bourgeois, Marie Brigitte; Bourgois, Didier; Brandily, Christian; Brandt, Pierre; Branquart, Frederic; Breilh, Patrick; Brilleman, Fabrice; Brisson, Thierry; Brocard, Francis; Bruel, Pierre; Brun, Jean Pierre; Buisson, Jean Gabriel; Buisson Virmoux, Isabelle; Bur, Christian; Cabal Malville, Elodie; Cabantous, Serge; Cabrol, Pierre; Cagnoli Gromovoi, Sylviane; Caillaux, Bruno Xavier; Caillot, Didier; Canchon Ottaviani, Isabelle; Canu, Philippe; Caramella, Alexana; Caramella, Alexane; Cardaillac, Christian; Carrivale, Alain; Cartal, Jean Pierre; Cassany, Bernard; Cauon, Bernard; Causeret, Jean Marie; Caye, Philippe; Cayet, Jean Paul; Cazor, Gilles; Cesarini, Joel; Chakra, Georges; Chambeau, Bernadette; Chambon, Valerie; Chanas, Jack; Chapuzot, Patrick; Charon, Ane; Charpin, Eric; Charton, Frederic; Cheikel, Jean; Chemin, Philippe; Chennouf, Kamel; Chequel, Henri; Chevrier, Denis; Ciroux, Patrick; Cissou, Yves; Claeys, Jean Luc; Clariond, Yves; Classen, Olivier; Cloerec, Ane; Clouet, Sophie; Cloup Lefeuvre, Anne Marie; Cochet, Chantal; Cocuau, Didier; Cohen, Henri; Cohen Presberg, Pascale Cohen; Colin, Stephane; Colin, Remy; Colucci, Robert; Come, Philippe; Condouret, Pierre; Conturie, Agnes; Corbin, Ane; Corticelli, Paola; Coste, Daniel; Cotrel, Olivier; Coueau, Sylvie; Coulon, Paul; Courdy, Christian; Courtin, Marc; Courtot, Pierre; Coutrey, Laurent; Couval, Rene; Cravello, Patrick; Cressey, Olivier; Cuisinier, Yves; Cunin, Bernard; Cunnington, Bernard; Cusseau, Herve; Cuvelier, Christian; Arailh, Bruno D.; Dabboura, Adib; Dages, Laurence; Dahmani, Noureddine; Dandignac, Jean Christophe; Daney, Dominique; Dannel, Bernard; Darbois, Dominique; Dareths, Philippe; Daubin, Daniel; David, Jean Claude; de Foiard, Patrick; de Mallmann Guyot, Veronique De; de Wit, Marie Astrid; Debast, Francoise; Deboute, Eric; Debuc, Jean Pierre; Dechoux, Edouard; Decloux, Olivier; Decruyenaere, Yannick; Dejans, Jacques Maurice; Delarue, Michel; Delattre, Xavier; Delmaire, Patrick; Denis, Lucien; Deschamps Ben Ayed, Myriam; Devins, Pascal; Dezou, Sylvie; Dieuzaide, Pierre; Dirheimer, Bertrand; Dominguez, Paul; Donadille, Florence; Dondain, Benoit; Doridan, Pierre; Ouhet, Pascal; Dubois, Arnaud; Dubois, Ane; Ducharme, Pascal; Duchez, Paul; Dulard, Catherine; Dumoulin, Marc; Duprey, Georges; Durand, Jacques; Mohamed, Ibrahim; Chehab, El; Emery, Bernard; Emmanuel, Georges; Ashari, Ghazaleh Esna; Evrard, Eric; Fargeot Lamy, Aleth; Farges, Jean Louis; Faucher, Patrick; Faucie, Alain; Faure, Yves; Favre, Jean Jacques; Felipe, Jean Louis; Feret, Daniel; Ferragu, Alain; Ferrandin, Gerard; Ferriot, Francois; Finelle, Laurent; Flond, Jacques; Foieri, Jean; Fol, Stephane; Fontaine, Brigitte; Forichon, Dominique; Foucry, Michel; Fournier, Jean Francois; Fregeac, Bernard; Fuchs, Martin; Gabriel, Franck; Gaimard, Didier; Gallois, Stephane; Garapon, Georges; Garas, Mamdouh; Garcia, Pierre; Garcia, Jean Michel; Garcia, Marie Pierre; Garman, Waddah; Garzuel, Dominique; Gaspard, Jean Marc; Gauci, Laurent; Gautheron, Patrick; Gauthier, Jacques; Gauthier Lafaye, Pierre Yves; Gay, Michel Charles; Gay Duc, Bernadette; Gayout, Olivier; Gegu, Yann; Gentile, Francois; Germain, Emmanuel; Gharbi, Gerard; Gigandet Tamarelle, Catherine; Gilardie, Alain; Gilles Verliat, Martine; Gillet, Thierry; Gnana, Philippe; Goguey, Alain; Gombert, Alain; Gonin, Bernard; Gonzales, Philippe; Goulesque, Xavier; Graba, Jean Marc; Granier, Alain; Greiner, Olivier; Groboz, Martial; Gromoff, Serge; Grossemy, Xavier; Grossi, Christian; Guenin, Frederic; Gueranger, Pierre; Guerin, Patrick; Guerineau, Jean Pierre; Guessous Zghal, Fathia; Guicheux, Dominique; Guillere, Jacqueline; Guyonnet, Gilles; Haddad, Samir; Hadj, Nordine; Hamani, Djamel; Hamm, Jacky; Hammoudi, Djamal; Harle, Xavier; Harnie Coussau, Pierre; Hazen, Richard; Hembert, Francois; Hemon, Pierre; Hergue, Michel; Hestin, Christian; Heyraud, Luc; Hindennach, Dieter; Hirot, Etienne; Ho Wang Yin, Chan Shing; Hocquelet Denis, Catherine; Hoppe, Patrice; Horovitz, Daniel; Hours, Jean Michel; Houta, Benjamin; Hua, Gerard; Hui Bon Hoa, Nicole; Humez, Philippe; Hurier, Michel; Husson, Gerald; Hyvernat, Guy; Ichard, Jean Francois; Impens, Claude; Iovescu, Decebal; Jacob, Philippe; Jacob, Gildas; Jacquemart, Jean Pierre; Jacquier, Philippe; Jahanshahi Honorat, Shideh; Jalladeau, Jean Francois; Jan, Luc; Jannel, Yves; Jarrige, Vincent; Jeremiasz, Richard; Annick Jestin Depond, Marie; Joseph, Michel; Joseph Henri Fargue, Helene; Joubrel, Alain; Jouet, Alain; Julien, Bruno; Jullien, Francois; Jullien, Jean Louis; Kadoche, David; Kahl, Etienne; Kanawati, Aiman; Khalife, Sami; Khettou, Christophe; Kiers, Jean Paul; Kissel, Christian; Klein, Jean Claude; Klopfenstein, Samuel; Koch, Alexis; Koenig, Georges; Kohler, Philippe; Koriche, Abdelmalek; Labernardiere, Nicole; Labet, Philippe; Lablanche, Fabien; Laborde Laulhe, Vincent; Lagorce, Xavier; Laine, Eric; Lalague, Pascal; Laleu, Jean Noel; Lambert, Michel; Lambert Ledain, Mireille Lambert; Lambertyn, Xavier; Lame, Jean Francois; Langlois, Frederic; Lanoix, Eric; Laprade, Michel; Lasseri, Charaf; Laterrade, Bernard; Laurent, Jean Claude; Laurier, Bernard; Laval, Laurent; Le Borgne, Patrick; Le Franc, Pierre; Le Henaff, Patrick; Le Noir de Carlan, Herve; Le Roy, Jean Pierre; Le Roy Hennion, Florence; Lebon, Louis; Lecler, Olivier; Leclerc, Philippe; Ledieu, Christian; Lefebvre, Bernard; Lefevre, Philippe; Lehujeur, Catherine; Leiber, Christian; Leick, Gerard; Lemberthe, Thierry; Lenevez, Norbert; Lenoble, Patrick; Leriche, Philippe; Leroux, Eric; Leroy, Jean Michel; Leroy, Christian; Lescaillez, Dominique; Leurele, Christian; Lhermann, Sophie; Libermann, Pierre; Licari, Gilbert; Lo Re, Antoine; Long, Philippe; Long, Jean Louis; Lormeau, Boris; Louchart, Jean Christophe; Lucas, Jean Pierre; Luquet, Thierry; Lussato, Philippe; Maarouf, Moustapha; Mabilais, Francois; Magnier Sinclair, Christine; Mahot Moreau, Pascale; Malafosse, Denis; Mandirac, Jean Paul; Manolis, Jerome; Mante, Jean Pierre; Maquaire, Claude; Marchal, Thierry; Marchand, Guillaume; Marillesse, Olivier; Marmier, Gabriel; Herve Maron, Yves; Marrachelli, Nadine; Marsaux, Michel; Martin, Bruno; Martin, Michel; Deiss, Pascale Martin; Masson, Arnaud; Mativa, Bruno; Matton, Jean Francois; Mauffrey, Jean; Mauriere, Serge; Maurois, Georges; Maury, Joceline; Mayer, Frederic; Menu, Pierre; Mercier, Bernard; Messmer, Daniel; Mestiri, Sami; Meyer, Gilles; Michaelides, Michael; Michaud, Gilles; Michenaud, Bernard; Mielot, Stephane; Millory Marco, Jerry Anne; Mingam, Stephane; Mira, Reginald; Mius, Stephane; Monnier Meteau, Marie Paule; Mora, Francis; Morbois Trabut, Louise; Morosi, Laurent; Mougeolle, Jean Luc; Mouget, Jean Louis; Mouroux, Daniel; Mouthon, Jean Marie; Muller, Jacques; Nakache, Ane; Narbonne, Herve; Navarranne Roumec, Anne; Navarro, Pierre; Neubrand, Jean Yves; Nguyen, Quang Thieu; Nguyen Quang, Guy; Nguyen Xuan, Thong; Niot, Patrice; Oudart, Jean Maurice; Outteryck, Alain; Pages, Jean Marie; Paillet, Charles; Pain, Jean Marie; Pangaud de Gouville, Patricia; Paquin, Olivier; Parent, Vincent; Parer Richard, Claire; Parrot, Francine; Parthenay, Pascal; Pascariello, Jean Claude; Passebon, Jean Claude; Pere, Alain; Perelstein, Laurent; Perot, Michel; Petit, Richard; Petit, Philippe; Petit, Francois; Petruzzi, Philippe; Phelipeau, Denis; Philippon, Jean Claude; Philippon, Gilles; Picard, Bruno; Picard, Jean Claude; Picot, Bernard; Piera, Jean Francois; Pieri, Alain; Piffoux, Eric; Pilard, Patrick; Pillet, Alain; Pinot, Philippe; Pinzani, Alain; Pleskof, Alain; Plessier, Jean Claude; Plisson, Alain; Pochon, Claude; Poggi, Valerie; Poirat, Alain; Poiree, Maurice; Polleux, Janick; Noel Pontecaille, Jean; Posocco, Regis; Pospiech, Jean Claude; Pradies, Felix; Prevot, Remi; Pueyo, Jean Bernard; Quaelli, Jacques; Rabbia, Michel; Rabemananjara, Aimery; Rami, Saad; Rapin, Jean Jacques; Rasquin, Corinne; Ratinaud, Didier; Reboud, Bruno; Reboul, Philippe; Reichman, Jean Jacques; Reinhardt, Patrick; Renard Houta, Catherine Renard; Reverdy, Olivier; Revol, Michel; Rey, Pierre Alain; Richardeau, Yves; Rives, Bernard; Robida, Christine; Rochez Fraiberg, Muriel; Rodet, Jean Pierre; Rolland, Jean Francois; Romand, Bruno; Romano, Jean Paul; Rosati Gretere, Chantal; Rosey, Alain; Rosset, Martial; Rossi, Jean Pierre; Rouquette, Georges; Rousseau, Michel; Rousselon, Xavier; Roy, Christophe; Royer, Denis; Ruetsch, Marcel; Saade, Maurice; Saby Kuchler, Nicolas; Samar, Guy; Sanchez, Pierre Yves; Sane, Alain; Sanz, Jean Paul; Sardon, Michel; Sarrazin, Marc Eric; Sasportes, Gilbert; Saudou, Francis; Sauze, Elisabeth; Savary, Pascal; Schenowitz, Alain; Schmartz, Pierre; Schoepfer, Marc Olivier; Seewagen, Jacques; Serramoune, Denis; Serre, Christian; Sicard Guroo, Helene; Sichãc, Jean Philippe; Sifaoui, Sylvain; Simoncello, Marc; Simonin, Marie Jeanne; Simonnet, Jean Francois; Spindler, Didier; Steier, Alain; Sultan, Charles Raphael; Taghipour, Kouroch; Talayrach, Bruno; Talbot, Francois; Talhouarn, Vanessa; Tallec, Yves; Tarasco Schenrey, Elisabeth; Tarrene, Michel; Tater, Dominique; Tessier, Bernard; Teste, Marie; Thierry, Dominique; Thiollier, Patrice; Thoreau, Frederic; Thual, Jean; Traen, Vincent; Trigano, Jacques Alexane; Troussier, Jean Bernard; Truong Ky Minh, Bernard; van Melckebeke, Gerard; Vaque, Philippe; Vaucelle, Celine; Vedel, Eric; Venu, Didier; Verdavoine, Patrick; Vergeron, Jean; Viallon, Philippe; Viault, Dominique; Vieules, Jean Max; Vigier, Jean Paul; Vilain, Jean Marie; Villard, Bruno; Vitoux, Jean Francois; Viviand, Paul; Vivien, Olivier; Walter, Patrice; Waquier, Patrick; Waszkiewicz, Jean Marc; Weidich, Stephane; Westerfeld, Raymond; Weynachter, Gerald; Wilhelm, Pierre; Wolff, Claude; Wursthorn, Marc; Zammattio, Didier; Zylinski, Bernard; Lauer, Peter; Kühn, Uwe; Weltzel, Wolfgang; Mohr, Hella; Weyland, Klaus; Spittel, Bärbel; Böhm, Günter; Ferdowsy, Said; Hanusch, Peter; Spiekermann, Josef; Albert, Edwin; Stuff, Karl; Jungmair, Wolfgang; Koller, Sabine; Schubert, Wilhelm; Schlehahn, Fred; Bormann, Gundula; Graf, Kristof; Stiehler, Gisela; Bock, Manfred; Müller, Angelika; Haufe, Michael; Nielsen, Lorenz; Raum, Doris; Rogler, Karin; Bürstner, Joachim; Völk, Hans-Jörg; Sachse, Michael; Escher, Torsten; Doumit, Adel; O'dey, Hildegard; Holzmann, Ulrike; Sauer, Hermann; Schellenberg, Gottfried; Carius, Jürgen; Dänschel, Wilfried; Kopf, Aneas; Zerr, Elena; Tatalovic, Ratko; Rupp, Heiun; Anders, Elfriede; Mende, Marion; Volk, Ulrich; Hagenow, Aneas; Lang, Thomas; Schmitz, Karl-Heinz; Gössling, Jan-Henik; Mutsch, Günther; Steidel, Joachim; Osten, Klaus; Giokoglu, Kiriakos; Bellisch, Sabine; Füll, Katja; Walther, Wolfgang; Flick, Sabine; Dünnebier, Rosemarie; Dharmawan, Ichsan; Schönmehl, Wolfgang; Hoss, Valentin; Kipping, Stephan; Wolf, Hans-Joachim; Wolf, Hans-Frieich; Willmann, Volker; Bugarski, Bruno; Hoffschröer, Josef; Von Wallfeld, Siegrun; Ruhland, Guun; Bulling, Daniel; Häusler, Maren; Haustein, Gabriele; Kallenbach, Cornelia; Schwemmler, Claudia; Frank, Antje; Lodder-Bender, Ulrike; Rawe, Klaus; Reinert, Hans-Ferdinand; Schönhof, Petra; Fahrenschon, Klaus; Schorcht, Elisabeth; Etzold, Erika; Brehm, Michael; Paust, Wolf-Dieter; Schulte-Kemna, Achim; Pötter, Klaus-Werner; Ott-Voigtländer, Ulrike; Schwenke, Reto; Thinesse-Mallwitz, Manuela; Siml, Steffi; Stern, Hirene; Roelen, Harald; Scherhag, Klaus-Peter; Matulla, Petra; Herrmann, Hans Joachim; Neumann, Gerhard; Barbuia, Marius; Vormann, Reinhold; Hitzler, Karl; Linum, Aneas; Hanke, Klaus; Hohberg, Hans-Joachim; Klingel, Roger; Hohnstädter, Rainer; Klasen, Hartmut; Aschermann, Peter; Grau, Wilfried; Killinger, Paul; Gross, Kathrin; Naus, Rainer; Todoroff, Karin; Zühlke, Wolfgang; Kellner, Hanns-Ulrich; Hager, Eva; Thieme, Jochen; Kornitzky, Michael; Rösch, Volker; Heinze, Elke; Hiederer, Wolfgang; Konz, Karl-Heinz; Köhler, Michael; Diekmann, Martin; Junghans, Edith; Dietermann, Friedgard; Kerp, Ekkehard; Schäfer-Lehnhausen, Silvia; Kruck, Irmtraut; Ettelt, Rolf; Hölscher, Aneas; Kittler, Sybil; Jung, Heiun; Mailänder, Albert; Nowara, Peter; Ritschl, Harald; Mödl, Bernhard; Gallwitz, Torsten; Meyer, Stephan; Peter, Anton; Peters, Otto; Pflaum, Petra; Fröhlich, Karl-Heinz; Mertens, Hans-Jürgen; Merlin-Sprünken, Verena; Erpenbach, Klaus; Fervers, Frank; Kuhl, Ulrike; Halsig, Friedemann; Rein, Wilfried; Hauser, Ernst-Richard; Laubenthal, Florin; Richard, Frank; Langer, Claus; Lange, Rainer; Eska, Jan; Mohanty, George; Lange, Isengard; Eltges, Nicole; Kuntz, Christoph; Mechery, Thomas; Vöckl, Josef; Viergutz, Christoph; Stähle-Klose, Claudia; Sohr, Katja; Böhler, Steffen; Brecke, Georg; Burls, Malcolm; Werner, Karl-Michael; Vorpahl, Ralf; Stahl-Weigert, Beate; Bunge, Gerd; Thomsen, Jutta; Blessing, Erwin; Bengel, Bengel; Buhlmann, Ulla; Tröger, Tröger; Sippel, Sippel; Vossschulte, Vossschulte; Wilms, Wilms; Appelt, Appelt; Dauterstedt, Dauterstedt; Witte, Witte; Böttger, Uta; Wyborski, Waltraud; Strache, Sabine; Böttger, Werner; Zeiner, Luise; Wuttke, Wanda; Stoidner- Amann, Annette; Stoermer, Brigitte; Bock, Stephan; Groos-März, Cornelia; Thamm, Maria-Elisabeth; Meier, Josef; Schneider, Martin; Niessen, Ulrich; Storm, Gernot-Rainer; Streitbürger, Elmar; Münkel, Thomas; Palfi, Mihai; Naumann, Ulrich; Tannhof, Gabriele; Streibhardt, Frank; Gebhardt, Wolfgang; Nieswandt, Gerhard; Gerke, Ulrich; Nöhring, Axel; Bott, Jochen; Goertz, Jutta; Winkler, Dietmar; Lotter, Edith; Kraaz, Katja; Bärwinkel, Petra; Hildebrandt, Diana; Weyers, Georg; Kubin-Siring, Birgit; Baier, Eduard; Weber, Thomas; Holz, Dirk-Egbert; Wolfers, Johannes; Kihm, Wolfgang; Kamali-Ernst, Schirin; Amann, Wolfgang; Kaase, Hans-Jürgen; Banning, Ottmar; Voigt, Thomas; Grünert, Frank; Gürtler, Michael; Pferdmenges, Karin; van Treek, Heiko; Möller, Bernd; Weigel, Sybille; Jun Hassler, Normann; Mauer, Helmuth; Beckers, Erwin; Weber, Clemens-August; Hawash, Hana; Ladke, Dietrich; Labitzky, Gerlinde; Kunkel, Petra; Hartung, Wolfgang; Pomykaj, Thomas; Prokop, Heiun; Schleif, Thomas; Cascino, Luisa; Exner, Petra; Daelman, Eric; Dietrich, Aneas; Prasse, Thomas; Brundisch, Stefanie; Schipper, Ralf; Duderstaedt, Bernd; de Haan, Fokko; Schmidt-Reinwald, Astrid; Seidel, Peter; Schmitz, Joachim; Bülent, Ergec; Ja Pique, Pyoong; Ding, Roland; Eggeling, Thomas; Duderstaedt, Elvira; Ferchland, Hans-Peter; Kruth, Renate; Gralla, Dieter; Köhler, Angelika; Laborge, Joachim Rene; Hammer, Harald; Richter, Ilona; Sauldie, Happy; Valk-Denkema, Inge Van Der; van der Valk, Leo; Feely, John; Dunne, Liam; Cox, John; Doyle, Michael; O'Gorman, Mary; Kennedy, John; Maher, Brian; Forde, Derek; Harrington, Peter; Cronin, Brian; Coady, Anew; Craig, John; O'Dowd, Caroline; O'Doherty, Brian; O'Connor, Patrick; Ling, Roland; Perry, Majella; Crowley, James; Keaveney, Lynda; Townley, Eadaoin; O'Shea, Eamonn; Regan, Michael; Cunningham, Seamus; Bluett, Desmond; Whyte, Oliver; Casey, Michael; Ruane, Fergal; Fitzgerald, Eleanor; O'Beirn, Eugene; Faller, Eamonn; Moffatt, Sean; Coleman, Michael; Day, Brendan; Mcadam, Brendan; O'Neill, Daragh; Mac Mahon, Conor; Wheeler, Mark; Byrne, Sheila; Fulcher, Kieran; CAREY, Owen; O'Connell, Kieran; Keane, Jack; Almarsomi, Laith; Vaughan, Carl; O'Callaghan, Tom; Grufferty, Tadgh; Shanahan, Eamon; Crowley, Brendan; Moran, Joe; Cotter, Jeremy; Healy, Colin; Curtin, Tom; Dillon, Joe; Dennehy, Thomas; Murphy, Elaine; Kennedy, Michael; Coffey, Donal; Carroll, Paul O.; Oliver, Barry; Mccarthy, Shane; Joyce, Peter; O'Shea, Gerard; Apperloo, A. J.; Basart, D. C. G.; Bax, M.; Beysens, P. A. J.; Breed, J. G. S.; Derks, A.; Eijgenraam, J. W.; Hermann, J. P. R.; Janus, C. L.; Kaasjager, H. A. H.; Klomps, H. C.; Koole, M. A. C.; Koster, T.; Kroon, C.; Lieverse, A. G.; Massaar-Hagen, B. E. M.; Moghaddam, F.; Oldenburg-Ligtenberg, P. C.; Potter van Loon, B. J.; Stroes, E. S. G.; Twickler, Th B.; van Asperdt, F. G. M. H.; van Asseldonk, J. P. M.; van der Loos, T. L. J. M.; van der Velde, R. Y.; van der Vring, J. A. F.; van Dorp, W. T.; van Essen, G. G.; van Kalmthout, P. M.; van Liebergen, R. A. M.; van Wissem, S.; Waanders, H.; Withagen, A. J. A. M.; Andersen, Per Vidar Klemet; Andersen, Randi F.; Andersson, Egil; Arnstad, Asle; Belguendouz, Larbi; Birkeland, Inge Arve; Bjørkum, Kari; Bredvold, Thor; Brevig, Leif Harald; Buchman, Erik; Burkeland-Matre, Rune; Burski, Krzysztoft; Byre, Roald; Bø, Per Erik; Dahl, Erik; Duch, Anna; Duong, Khoa; Dvergsdal, Peter; Edvardsen, Magne; Ernø, Asbjørn; Fredwall, Svein Otto; Glasø, Morten; Glasø, Jan; Grini, Asbjørn; Hallaråker, Arne; Normann Hansen, Age Normann; Haugland, Helge Haugland; Henrichsen, Svein Høegh; Hestnes, Atle; Idehen, Norman I. E.; Jacobsen, Kristin Løland; Johansen, Ture; Johnsen, Roald; Jonasmo, Kåre; Kirknes, Svetalana; Kjetså, Arild; Kjaer, Peter; Knoph, Erik; Knutssøn, Carl; Koss, Arne; Kravtchenko, Oleg; Krogsæter, Dagfinn; Langaker, Kåre; Lind, Knut W.; Lund, Kjell Rømyhr; Madsbu, Sverre; Mehlum, Yvonne E. Mazurek; Moon, Philipp; Movafagh, Aram; Myhrer, Kurt; Nørager, Dan Michael; Ore, Stephan; Rafat, Hooshang B.; Rød, Reinert; Schmidt-Melbye, Torgeir; Singh, Navneet; Singsås, Tore; Skjelvan, Gunnar; Smet, Arthur; Staalesen, Staale; Storeheier, Espen; Storhaug, Sidsel; Storm-Larsen, Ane; Sundby, Jon Eivind; Syverstad, Dag Eivind; Sørensen, Anne Sissel; Torjusen, Trygve B.; Torkelsen, Arne; Tunby, Jan Reidar; Vanberg, Pål Johan; Vevatne, Audun; Vikse, Arild; Wahlstrøm, Viktor; Walaas, Kirsten; Walløe, Arne Eyolf; Wear-Hansen, Hans-Gunnar; Ole Ystgaard, Ole Aneas; Zimmermann, Birgit; Øvsthus, Knut; Aião, Julio; Albuquerque, Mario; Alves, Fernando; Esteves, Antonio; Amaral, Maria Fatima; Amaral, Fátima; Amorim, Helena; Anade, Benilde; Anade, Maria Benilde; Antonio, Godinho; Araujo, Francisco; Arriaga, Antonio; Baeta, Sonia; Afonso, Francisca Banha; Beato, Vitor; Beirão, Paula; Martins, Ausenda Belo; Bernardes, Jose; Botas, Luis; Baeta, Antonio; Ramos, Manuel Braga; Brandão, Peo; Brandão, Antonio G.; Brandão, Antonio; Raposo, Antonio Caetano; Carrilho, Francisco; Carvalho, Isabel; Carvalho, Patricia; Castel-Branco, Ana; Castellano, Maria Desamparados; Corredoura, Ana; Corredoura, Ana Sofia; Costa, Vitor; Coutinho, João; Crujo, Francisco; Cunha, Damião; Dias, Manuela; Fernandes, Maria Emilia; Ferreira, Gustavo; Ferreira, Dirce; Ferreira, Jorge; Ferreira, Antonio M.; Fonseca, Antonio; Freitas, Paula; Gago, Amandio; Galego, Rosa; Garrett, Antonio Viriato; Gavina, Cristina; Simões, José Geraldes; Gomes, Maria Fatima; Gomes, Norberto; Gomez, Brigitte; Graça, Peo; Gravato, Antonio; Guedes, Nuno Filipe; Guerra, Fernanda; Issa, Custódio; João, Isabel Fernandes; João, Isabel; Jorge, Vasco; Leite, Maria Salome; Lousada, Nuno; Macedo, Filipe M.; Madeira Lopes, João; Magalhães, Jorge; Marinho, Jose Carlos; Marques, Carlos; Marques, Jose Augusto; Marques Ferreira, Antonio; Martins, Jose Carlos; Martins, J. Belo; Matos, Alice; Melo, Miguel; Miguel, Antonia; Monteiro, Filomena; Monteiro, Francisco; Monteiro, Filomena B.; Sarmento, João Morais; Morato Sá, Maria José; Mota, Joana; Moura, Luis; Moura, Brenda; Neves, Lena; Neves, Celestino; Oliveira, Maria; Oliveira Ramos, Manuel; Osorio, Ramos; Pacheco, Joao; Palma, Isabel; Peixoto, Maria Cristina; Pereira, Helder; Pestana, João; Pignatelli, Duarte; Pinho, Hernani; Puig, Jorge; Raindo, Maria; Ramos, Helena; Rebelo, Marta; Roigues, Antonio; Roigues, Alvaro; Roigues, Elisabete; Rola, José; Rovytchcva, Milena; Sa, João; Santos, Fernando; Santos, João Cesar; Sequeira Duarte, Joao; Serra E Silva, Polybio; Silva, Bernardino; Silva, Paula; Silva, Maria; Silva, Francisco; Silva, Dora; Silva, José; Silvestre, Isabel; Simões, Heleno; Soares, Manuela; Sousa, Nelson; Sousa, Antonio; Souto, Delfina; Teixeira, Esmeralda; Torres, Isabel; Valle, Tahydi; Ventura, Carlos; Vicente, Ana; Vieira, Muriel; Alfaro, Rafael; Alonso, Roigo; Alvarez, Juan Carlos; Allut, Germán; Amado, Jose A.; Ampuero, Javier; Angel, Luis Fernando; Antolín, Eduardo; Anton, Javier; Aranda, Jose Luis; Argimon, Jordi; Arques, Francesc; Arribas, Jose Peo; Arroya, Concepción; Arroyo, Jose Antonio; Auladell, Maria Antonia; Bajo, Julian; BALVIN, Alberto; Ballester, Jose Vicente; Barreda Glez, Maria Jesus; Becerra, Antonio; Bermejo, Juan Carlos; Bernacer, Luis; Besada, Ricardo; Blasco, Jesús; Bravo, Manuel; Bueno, Francisco Manuel; Campo, Ignacio; Carrasco, Jose Luis; Catalán, José Ignacio; Cobo, Jose; Coello, Ignacio; Combarro, Jesús; Contreras, Juan A.; Correa, Julian; Cortilla, Alberto; Cuatrecasas, Guillem; Chicharro, Sana; de Dios, Juan; de Los Arcos, Enrique; de Portugal, Jose; del Cañizo, Francisco; del Molino, Fatima; Díaz, Jose Luis; Domingo, Javier; Escobar, Carlos; Escoda, Jaume; Espinosa, Eugenio; Ester, Francisco; Fernandez, Antonio; Ferreiro, Manuel; Fondas, Jose Maria; Fraile, Angel Luis; Franco, Miguel; Fuentes, Francisco; Garcia, Jose Antonio; Garcia, Domingo; Garcia, Manuel Enrique; García, Luis; Garcia, Jesus; Gilabert, Rosa; Goiria, Begoña; Gomez, Purificación; Gomez-Calcerrada, David; Gonzalez, Manuel; Gonzalez, Jose Manuel; Guijarro, Carlos; Guirao Gujarro, Victor; Herrera, Carlos; Herrera, Maria Carmen; Herrero, Miguel; Ibarguren, Amaya; Irigoyen, Luis; Jimenez, Blas; Lamelas, Jose Antonio; Laplaza, Ismael; Laporta, Felix; Lazo, Victor; Leal, Mariano; Ledesma, Vicente; Lopez, Peo; Lopez, Pablo; Lopez, Alberto; López, Maria Jose; Lopez-Cepero, Eduardo; Lorenzo, Francisco; Lucena, Javier; Luquín, Rafael; Lloveras, Ariadna; Maceda, Teresa; Macia, Ramon; Marti, Cristina; Martin, Jose Maria; Martin, Isodoro; Martín Lesende, Iñaki; Martinez, Mercedes; Martinez, Juan Alberto; Martinez, Peo; Martinez, Angel; Mato, Fernando; Medel, Federico; Mederos, Ana Maria; Mediavilla, Javier; Mediavilla, Gregorio; Mestron, Antonio; Michans, Antonio; Millán, Jesús; Molina, Carlos; Monroy, Carmelo; Monte, Inés; Montes, Jose Maria; Morales, Clotilde; Morales, Francisco J.; Morata, Carmen; Mori, Carlos; Muñoz, Jaime; Muñoz, Maria Jose; Núnez, Julio; Nuñez, Alfonso; Ocaña, Fermin; Olaz, Fernando; Ollero Artigas, Anes; Ortega, Juan; Oteo, Olga; Pascual, Jose Maria; Paya, Jose Antonio; Pechuan, Joaquín; Penedo Suarez, Ramón; Perez, Eugenia; Pesquera, Carlos; Pia, Gonzalo; Piea, Maria; Pinilla, Martin; Pita, Alejano; Pose, Antonio; Prieto Díaz, Miguel Angel; Quesada, Carmen; Ramirez, Francisco; Ramirez, Carmen; Ramirez, Luisa; Reinares, Leonardo; Rey, Salvador; Ribas, Montse; Ridaura, Amparo; Ridocci, Francisco; Rigueiro, Peo; Rivera, Salomón; Robles, Antonio; Rodero, Estrella; Roiguez, Jose Angel; Romero, Fernando; Romero Hernandez, Franklin; Romeu, Regina; Rubio Buisán, Lorenzo; Salas, Fernando; Sánchez, Carlos; Sánchez, Jesus; Saponi, Jose Maria; Serres, Miguel; Suarez, Saturnino; Suarez, Carmen; Tato, Maria; Tebar, Francisco Javier; Toda, Maria Roca; Tofe, Santiago; Urdiain, Raquel; Vaamonde, Leopoldo; Valderrama, Javier; Vazquez, Jose Antonio; Velazquez, Osvaldo; Venell, Federico; Vilariño, Ruben; Villa, Maria Jesus; Villar, Maria Dolores; Zarauza, Jesus; Zuñiga, Manuel; Abab, Jose Luis; Abad, Eduardo; Abad, Rafael; Afonso, Carmen; Aguilar, Gerardo; Alberiche, Maria Del Pino; Alcolea, Rosa; Alegria, Eduardo; Almagro, Fátima; Almenara, Africa; Almenos, Maria Cruz; Alonso, Javier; Alvarez, Manuel; Ampudia, Javier; Andia, Victor Manuel; Anglada, Jordi; Aranda, Miguel Ángel; Arbelo, Lorenzo; Armengol, Francesc; Arnau, Asunción; Arrarte, Vicente; Arribas, Bienvenido; Artiñano, Yolanda; Avilés, Benjamín; Ayensa, Javier; Ballestar, Enric; Ballester, Javier; Barcelo, Bartolome; Barcena, Felix; Barranco, Mercedes; Barrena, Isabel; Barriales, Vicente; Barrot, Joan; Bartolome, Jose A.; Belmonte, Joan; Bellés, Amadeo; Benito, Josefina; Bernad, Antonio; Biendicho, Armando; Blanco, Rubén; Boix, Evangelina; Bonora, Carlos; Boxó, Jose Ramon; Brea, Angel; Caballero, Peo; Cabrera, Peo; Cabrero, Juan Jose; Calduch, Lourdes; Calero, Francisco; Calvo Garcia, Jose Javier; Camacho, Jose; Canales, Juan Jose; Caparros, Jorge; Carbonell, Francisco; Caro, Manuel; Castilla, Miguel Angel; Castillo, Luis; Cepero, Daniel; Cerdan, Miguel; Cimbora, Antonio; Civera, Miguel; Colchero, Justo; Comas Fuentes, Angel; Corpas, Clara; Corrales, Juan Antonio; Cotobal, Eusebio; Cruz, Carmen; Cruz, Inmaculada; de La Flor, Manuel D.; de Luis, Alberto; del Alamo, Alberto; del Rosario, Victor; Diego, Carlos; D'Lacoste, Marta; Doganis Peppas, Constantino; Dominguez, Jose Ramon; Durá, Francisco Javier; Durand, Jose L.; Ena, Javier; Encinas, Ana Rosa; Erdozain, Juan Peo; Escribano, Jose; Escriva, Blanca; Esteve, Eduardo; Facila, Lorenzo; Fenoll, Federico; Fernandez, Eugenio; Fernandez, Celia; Fernandez, Maria Jesus; Fernandez, Antonia; Fernandez, Jacinto; Fernandez, Severo; Fernandez, Jose Manuel; Fernandez, Jose Manuel Fernandez; Ferrer, Juan Carlos; Ferrer, Peo; Ferrer Bascuñana, Peo; Fierro, Maria Jose; Flores, Julio; Fuentes, Fernando; Fuertes, Jorge; Galgo, Alberto; Galvez, Angel; Gallego, Anea; Garcia, Maria Angeles; Garcia, Jose; Garcia, Maria Luisa; Garcia, Peo; Garcia, Javier; García, Francisco; Garrido, Nícolas Garrido; Gil, Manuel Gil; Ginés Gascón, Ramón; Godoy, Diego; Gomez, Carlos Manuel; Gonzalez, Miguel; Gonzalez, Rosa; Gonzalez, Rocío; Gonzalez, Enrique; Gonzalez, Juan Jose; Gonzalez, Joaquin; Gonzalez Huambos, Adan; Guerrero, Jordi; Guillen, Rosario; Guirao, Lorenzo; Gutierrez, Fernando; Gutierrez, Diego; Hernandez, Alberto; Hernandez, Antonio; Hernandis, Vicenta; Herrero, Jose Vicente; Herreros, Benjamin; Hevia Roiguez, Eduardo; Horgue, Antonio; Illan, Fatima; Inigo, Pilar; Ibrahim Jaber, Ali; Jimenez, Manuel; Jornet, Agusti; Juanola, Ester; Laguna, Alfonso; Latorre, Juan; Lebron, Jose Antonio; Lecube, Albert; Ledesma, Claudio; Ligorria, Cristina; Lima, Joan; López, Jose Enrique; Lopez, Manuel; López, José Antonio; López, Jaime; López, Isio; Lozano, Jose Vicente; Mangas, Miguel Angel; Mangas, Alipio; Manzano, Antonio; Maraver, Juan; Marco, Maria Dolores; Marchán, Enrique; Marchante, Francisco; Marin, Fernando; Marreo, Josefa Esther; Martin, Manuel; Martin, Alberto; Martin, Francisco Javier; Martinez, Antonio; Martinez, Guillermo; Martínez, Luis; Martinez Barselo, Antonio Pablo; Mas, Emili; Mascareño, Isabel; Mascarós, Enrique; Massa, Rita; Mazón, Pilar; Mediavilla, Juan Diego; Mena, Candido; Mendez, Jose; Mendez, Jose Maria; Mezquita Raya, Peo; Millan, Jose Maria; Millaruelo, Jose; Minguela, Ester; Miret, Pere; Molina, Mariano; Molina, Carmen; Montagud, Blanca; Montalban, Coral; Montiel, Angel; Montoro, Javier; Monze, Bernardo; Moreno, Francisco Luis; Morillas, Antonio; Moro, Jose Antonio; Moya, Ana; Muñiz, Ovidio; Muñoz, Manuel; Navarro, Vicente Luis; Nerin, Jesus; Nicolas, Ricardo; Nogueiras, Concepción; Ojeda, Benito; Olmerilla, Javier; Oller, Guillermo; Ortega, Antonio; Ortega, Manuel; Ortega, Miguel; Ortiz, Maria Jose; Otegui Alarduya, Luis; Palet, Jordi; Palomo, Jesus; Paytubí, Carlos; Peiro, Rafael; Pelaez, Carmen; Peña, Peo; Peñafiel, Javier; Perez, Antonia; Perez, Elvira; Perez, Tomas; Peso, Miguel; Pilar, Juan Manuel; Piñeiro, Carlos; Plaza, Jose Antonio; Polo, Noelia; Portal, Maria; Prieto, Jesus; Prieto, Luis; Prieto Novo, Manuel; Puñal, Peo; Quesada, Miguel; Quindimil, Jose Antonio; Rabade, Jose Manuel; Ramila Beraza, Luis Antonio; Ramirez, José Manuel; Ramos, Jose Antonio; Ramos, Francisco; Rayo, Manuel; Reixa Vizoso, Sol; Reyes, Antonio; Rico, Miguel Angel; Ripoll, Tomas; Rivera, Antonio; Robres, Mariano; Rodilla, Enrique; Roiguez, Miguel Angel; Roiguez, Zoilo Jesus; Roiguez, Carlos; Roiguez, Pilar; Roiguez, Melchor; Roiguez, Alfonso; Rojas, Domingo; Rosell, Luis; Rossignoli, Carlos; Rueda, Antonio; Rueda, Eloy; Ruix, Anes; Ruiz, Jose Antonio; Ruiz, Luis; Saban, Jose; Saez, Francisco Jose; Salleras, Narcis; Sánchez, Gerardo; Sanchez, Gloria; Sanchez, Angel; Sanfeliu, Josep Maria; Sangros Gonzalez, Javier; Santos, Francisco; Santus, Eufrosina; Sebastian, Alfredo; Seguro, Maria Eugenia; Selles, David; Serrano, Daniel; Serrano, Soledad; Serrano, Adalberto; Sestorain, Francisco; Solbes, Ruben; Soriano, Cristina; Suárez, Héctor; Surroca, Maria Luisa; Tarabini, Ada; Tarraga, Peo; Teixido, Eulalia; Terron, Raquel; Torres, Antonio; Tortosa, Jose Maria; Tortosa, Frederic; Valdés, Carmen; Valdés, Peo; Valiente, Jose Ignacio; Varo, Antonio; Vazquez, Enrique; Vázquez, Luis; Vela Ruiz de Morales, Jose Manuel; Vericat, Antonio; Vicioso, Peo; Vilaplana, Carlos; Villazón, Francisco; Lidia Viñas, Lidia Viñas; Zuagoitia, Jose Felix; Nörgaard, Faris; Dziamski, Ryszard; Haglund, Lars; Holm, Daniela; Sars, Mikael; Jagunic, Ivica; Östgård, Per; Kumlin, Lars; Jacobsson, Michael; Hamad, Yousef; Jäger, Wanje; Särhammar, Lars; Olsson, Anders; Boldt-Christmas, Antonina; Nyborg, Karin; Kjellström, Thomas; Ghazal, Faris; Wikström, Lene; Holby, Torulf; Bhiladvala, Pallonji; Kynde, Sara Maria; Eizyk, Enrique Julio; Tengblad, Anders; Christoffersson, Ole; Sjöström, Astrid; Kynde, Christian; Katzman, Per; Tenhunen, Anita; Lennermo, Klas; Lindholm, Carl-Johan; Löndahl, Magnus; Elfstrand, Aino; Grönlund-Brown, Inger; Ziedén, Bo; Minnhagen, Karin; Lindvall, Peter; Fant, Kristina; Kaczynski, Jacek; Wallmark, Anders; Wallén, Carl-Erik; Wallberg, Håkan; Grönquist, Lennart; Hansen, John Albert; Björkander, Inge; Timberg, Ingar; Rosenqvist, Ulf; Fries, Robert; Carlsson, Jan-Erik; Rautio, Aslak Tauno; Sjöberg, Lennart; Wirdby, Alexander; Höök, Peter; Larsson, Åsa; Bergström, Catharina Lysell; Jwayed, Addnan; Smolowicz, Adam; Lindman, Anne-Christine; Nilsson, Per; Tarrach, Gerrit; Carlsson, Ingolf; Wieloch, Mattias; Rindevall, Peter; Strömblad, Gunnar; Holmberg, Göran; Shahnazarian, Henrik; Melchior, Jan; Younan, Kamal; Hansson, Anders; Bjurklint, Dag; Borgencrantz, Bertil; Sjöström, Malin; Mullaart, Mikael; Munoz, Marjatta; Jakkola, Vallentina; Romot, Jaan; Dash, Rabinarayan; Magnusson, Jan-Olof; Ahmed, Saman; Jonsson, Christina; Pipkorn, Owe; Bray, Edward; Wolff, Aneas; Black, Iain; Head, Christopher; Allan, Anthony

    2011-01-01

    To assess the prevalence of persistent lipid abnormalities in statin-treated patients with diabetes with and without the metabolic syndrome. This was a cross-sectional study of 22,063 statin-treated outpatients consecutively recruited by clinicians in Canada and 11 European countries. Patient

  11. Predictors of non-adherence to and non-persistence with statin therapy among patients on oral diabetes medication in the netherlands: A retrospective inception cohort study

    NARCIS (Netherlands)

    Alfian, Sofa Dewi; Worawutputtapong, Pawida; Schuiling-Veninga, Catharina C.M.; Van Der Schans, Jurjen; Bos, Jens H.; Hak, Eelko; Denig, Petra

    2017-01-01

    Background: The use of statins as the most extensively used lipid-lowering therapy is known to be suboptimal in daily practice. Few studies, however, have looked at non-adherence and non-persistence as distinct phenomena. Objectives: To evaluate statin adherence and persistence rate, and to identify

  12. Data mining approach to predict BRCA1 gene mutation

    Directory of Open Access Journals (Sweden)

    Olegas Niakšu

    2013-09-01

    Full Text Available Breast cancer is the most frequent women cancer form and one of the leading mortality causes among women around the world. Patients with pathological mutation of a BRCA gene have 65% lifelong breast cancer probability. It is known that such patients have different cause of illness. In this study, we have proposed a new approach for the prediction of BRCA mutation carriers by methodically applying knowledge discovery steps and utilizing data mining methods. An alternative BRCA risk assessment model has been created utilizing decision tree classifier model. The biggest challenge was a very small size and imbalanced nature of the initial dataset, which have been collected by clinicians during 4 years of clinical trial. Iterative optimization of initial dataset, optimal algorithms selection and their parameterization have resulted in higher classifier model performance, with acceptable prediction accuracy for the clinical usage. In this study, three data mining problems have been analyzed using eleven data mining algorithms.

  13. Data mining-aided materials discovery and optimization

    Directory of Open Access Journals (Sweden)

    Wencong Lu

    2017-09-01

    Full Text Available Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper, and an introduction to the materials data mining (MDM process is provided using case studies. Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery, design, and optimization. State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co3O4 superstructures, materials design of layered double hydroxide, battery materials discovery, and thermoelectric materials design. The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation, and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.

  14. Data mining approach to model the diagnostic service management.

    Science.gov (United States)

    Lee, Sun-Mi; Lee, Ae-Kyung; Park, Il-Su

    2006-01-01

    Korea has National Health Insurance Program operated by the government-owned National Health Insurance Corporation, and diagnostic services are provided every two year for the insured and their family members. Developing a customer relationship management (CRM) system using data mining technology would be useful to improve the performance of diagnostic service programs. Under these circumstances, this study developed a model for diagnostic service management taking into account the characteristics of subjects using a data mining approach. This study could be further used to develop an automated CRM system contributing to the increase in the rate of receiving diagnostic services.

  15. Fuzzy conditional random fields for temporal data mining

    Science.gov (United States)

    Nurma Yulita, Intan; Setiawan Abdullah, Atje

    2017-10-01

    Temporal data mining is one of the interesting problems in computer science and its application has been performed in a wide variety of fields. The difference between the temporal data mining and data mining is the use of variable time. Therefore, the method used must be capable of processing variables of time. Compared with other methods, conditional random field has advantages in the processing variables of time. The method is a directed graph models that has been widely applied for segmenting and labelling sequence data that appears in various domains. In this study, we proposed use of Fuzzy Logic to be applied in Conditional Random Fields to overcome the problems of uncertainty. The experiment is compared Fuzzy Conditional Random Fields, Conditional Random Fields, and Hidden Markov Models. The result showed that accuracy of Fuzzy Conditional Random Fields is the best.

  16. Teaching Financial Data Mining using Stocks and Futures Contracts

    Directory of Open Access Journals (Sweden)

    Gary Boetticher

    2005-06-01

    Full Text Available Financial data mining models is considered to be "the hardest way to make easy money." Data miners are certainly motivated by the prospect of discovering a financial "Holy Grail." However, designing and implementing a successful model poses many intellectual challenges. These include securing and cleaning data; acquiring a sufficient amount of financial domain knowledge; bounding the complexity of the problem; and properly validating results. Teaching financial data mining is especially difficult due to the student's limited financial domain knowledge and the relatively short period (one semester for building financial models. This paper describes an application of a financial data mining term project based on Stock and E-Mini futures contracts and discusses "lessons learned" from assigning similar term projects over six different semesters. Results of each case study results are presented and discussed.

  17. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

    DEFF Research Database (Denmark)

    Postmus, Iris; Warren, Helen R; Trompet, Stella

    2016-01-01

    BACKGROUND: In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation. METHODS AND RESULTS: We perform...

  18. Clinical Profile of Statin Intolerance in the Phase 3 GAUSS-2 Study

    NARCIS (Netherlands)

    Cho, Leslie; Rocco, Michael; Colquhoun, David; Sullivan, David; Rosenson, Robert S.; Dent, Ricardo; Xue, Allen; Scott, Rob; Wasserman, Scott M.; Stroes, Erik

    2016-01-01

    Recent evidence suggests that statin intolerance may be more common than reported in randomized trials. However, the statin-intolerant population is not well characterized. The goal of this report is to characterize the population enrolled in the phase 3 Goal Achievement after Utilizing an

  19. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

    NARCIS (Netherlands)

    D. Postmus (Douwe); H. Warren (Helen); S. Trompet (Stella); B.J. Arsenault (Benoit J.); C.L. Avery; J.C. Bis (Joshua); D.I. Chasman (Daniel); C.E. de Keyser (Catherina Elisabeth); H. Deshmukh (Harshal); D.S. Evans (Daniel); Feng, Q. (QiPing); X. Li (Xiaohui); Smit, R.A.J. (Roelof A.J.); A.V. Smith (Albert Vernon); F. Sun (Fangui); K.D. Taylor (Kent); A.M. Arnold (Alice M.); M.J. Barnes (Michael); B.J. Barratt (Bryan J.); J. Betteridge (John); S.M. Boekholdt (Matthijs); E.A. Boerwinkle (Eric); B.M. Buckley (Brendan M.); Y.D. Chen (Y.); A.J. de Craen (Anton); S. Cummings; Denny, J.C. (Joshua C.); G.P. Dubé (Gregory); P.N. Durrington (Paul); G. Eiriksdottir (Gudny); I. Ford (Ian); X. Guo (Xiuqing); T.B. Harris (Tamara); S.R. Heckbert (Susan); A. Hofman (Albert); G. Kees Hovingh; J.J.P. Kastelein (John); Launer, L.J. (Leonore J.); Liu, C.-T. (Ching-Ti); Y. Liu (YongMei); T. Lumley (Thomas); P.M. Mckeigue (Paul); P. Munroe (Patricia); A. Neil (Andrew); D.A. Nickerson (Deborah); F. Nyberg (Fredrik); E. O'Brien (Eoin); C.J. O'Donnell (Christopher); W.S. Post (Wendy S.); N.R. Poulter (Neil); R.S. Vasan (Ramachandran Srini); K.M. Rice (Kenneth); S.S. Rich (Stephen); F. Rivadeneira Ramirez (Fernando); N. Sattar (Naveed); P. Sever (Peter); S. Shaw-Hawkins (Sue); D.C. Shields (Denis C.); P.E. Slagboom (Eline); N.L. Smith (Nicholas); J.D. Smith (Joshua D.); N. Sotoodehnia (Nona); A. Stanton (Alice); D.J. Stott (David. J.); B.H.Ch. Stricker (Bruno); T. Stürmer; A.G. Uitterlinden (André); W.-Q. Wei (Wei-Qi); R.G.J. Westendorp (Rudi); E.A. Whitsel (Eric A.); K.L. Wiggins (Kerri); R.A. Wilke (Russell A.); C. Ballantyne (Christie); H.M. Colhoun (H.); L.A. Cupples (Adrienne); O.H. Franco (Oscar); V. Gudnason (Vilmundur); G.A. Hitman (Graham); C.N.A. Palmer (Colin); B.M. Psaty (Bruce); P.M. Ridker (Paul); J.M. Stafford (Jeanette M.); Stein, C.M. (Charles M.); J.-C. Tardif (Jean-Claude); M. Caulfield (Mark); J.W. Jukema (Jan Wouter); Rotter, J.I. (Jerome I.); R.M. Krauss (Ronald)

    2016-01-01

    textabstractBackground In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Interindividual variation in HDL-C response to statins may be partially explained by genetic variation. Methods and results We

  20. Genetic determinants of statin intolerance

    Directory of Open Access Journals (Sweden)

    Pollex Rebecca L

    2007-03-01

    Full Text Available Abstract Background Statin-related skeletal muscle disorders range from benign myalgias – such as non-specific muscle aches or joint pains without elevated serum creatinine kinase (CK concentration – to true myositis with >10-fold elevation of serum CK, to rhabdomyolysis and myoglobinuria. The genetic basis of statin-related muscle disorders is largely unknown. Because mutations in the COQ2 gene are associated with severe inherited myopathy, we hypothesized that common, mild genetic variation in COQ2 would be associated with inter-individual variation in statin intolerance. We studied 133 subjects who developed myopathy on statin monotherapy and 158 matched controls who tolerated statins without incident or complaint. Results COQ2 genotypes, based on two single nucleotide polymorphisms (SNP1 and SNP2 and a 2-SNP haplotype, all showed significant associations with statin intolerance. Specifically, the odds ratios (with 95% confidence intervals for increased risk of statin intolerance among homozygotes for the rare alleles were 2.42 (0.99 to 5.89, 2.33 (1.13 to 4.81 and 2.58 (1.26 to 5.28 for SNP1 and SNP2 genotypes, and the 2-SNP haplotype, respectively. Conclusion These preliminary pharmacogenetic results, if confirmed, are consistent with the idea that statin intolerance which is manifested primarily through muscle symptoms is associated with genomic variation in COQ2 and thus perhaps with the CoQ10 pathway.

  1. DATA MINING THE GALAXY ZOO MERGERS

    Data.gov (United States)

    National Aeronautics and Space Administration — DATA MINING THE GALAXY ZOO MERGERS STEVEN BAEHR, ARUN VEDACHALAM, KIRK BORNE, AND DANIEL SPONSELLER Abstract. Collisions between pairs of galaxies usually end in the...

  2. IT Data Mining Tool Uses in Aerospace

    Science.gov (United States)

    Monroe, Gilena A.; Freeman, Kenneth; Jones, Kevin L.

    2012-01-01

    Data mining has a broad spectrum of uses throughout the realms of aerospace and information technology. Each of these areas has useful methods for processing, distributing, and storing its corresponding data. This paper focuses on ways to leverage the data mining tools and resources used in NASA's information technology area to meet the similar data mining needs of aviation and aerospace domains. This paper details the searching, alerting, reporting, and application functionalities of the Splunk system, used by NASA's Security Operations Center (SOC), and their potential shared solutions to address aircraft and spacecraft flight and ground systems data mining requirements. This paper also touches on capacity and security requirements when addressing sizeable amounts of data across a large data infrastructure.

  3. Visual Data Mining Toolbox, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Visual Data Mining (VDM) is an Internet-based software that supports spatial and temporal analyses of multimodal NASA science data including satellite images and...

  4. Data Mining and Homeland Security: An Overview

    National Research Council Canada - National Science Library

    Seifert, Jeffrey W

    2007-01-01

    .... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

  5. Data Mining and Homeland Security: An Overview

    National Research Council Canada - National Science Library

    Seifert, Jeffrey W

    2006-01-01

    .... In the context of homeland security, data mining can be a potential means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists...

  6. Data Mining and Homeland Security: An Overview

    National Research Council Canada - National Science Library

    Seifert, Jeffrey W

    2007-01-01

    .... In the context of homeland security, data mining can be a potential means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists...

  7. Data mining and business analytics with R

    CERN Document Server

    Ledolter, Johannes

    2013-01-01

    Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining

  8. Challenges in computational statistics and data mining

    CERN Document Server

    Mielniczuk, Jan

    2016-01-01

    This volume contains nineteen research papers belonging to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors’ contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The book’s related and often interconnected topics, represent Jacek Koronacki’s research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.

  9. Data Mining Solutions for the Business Environment

    OpenAIRE

    Ruxandra-Stefania PETRE

    2013-01-01

    Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The p...

  10. Data Mining Research for Information Security

    Science.gov (United States)

    2016-01-29

    AFRL-AFOSR-JP-TR-2016-0028 Data Mining Research for Information Security Kevin Barton Texas A&M University-San Antonio Final Report 01/29/2016...Final 3.  DATES COVERED (From - To)      20-05-2014 to 19-05-2015 4.  TITLE AND SUBTITLE Data Mining Research for Information Security 5a.  CONTRACT

  11. Data mining theories, algorithms, and examples

    CERN Document Server

    Ye, Nong

    2013-01-01

    AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable anal

  12. Statin use and survival following glioblastoma multiforme

    DEFF Research Database (Denmark)

    Gaist, David; Hallas, Jesper; Friis, Søren

    2014-01-01

    AIM: While some studies indicate a potential chemopreventive effect of statin use on the risk of glioma, the effect of statins on the prognosis of brain tumours has not yet been examined. We thus conducted a cohort study evaluating the influence of statin use on survival in patients...... with glioblastoma multiforme (GBM). METHODS: We identified 1562 patients diagnosed with GBM during 2000-2009 from the Danish Cancer Registry and linked this cohort to Danish nationwide demographic and health registries. Within the GBM cohort, each patient recorded as using statins prior to diagnosis (defined as ≥2...... redeemed prescriptions) was matched to two statin non-users (

  13. Statins intake and risk of liver cancer: A dose-response meta analysis of prospective cohort studies.

    Science.gov (United States)

    Yi, Changhong; Song, Zhenggui; Wan, Maolin; Chen, Ya; Cheng, Xiang

    2017-07-01

    Previous studies have indicated that statins intake was associated with liver cancer risk, but presented controversial results.Studies in PubMed and EMBASE were searched update to February 2017 to identify and quantify the potential dose-response association between statins intake and liver cancer.Six eligible studies involving a total of 11,8961 participants with 9530 incident cases were included in this meta-analysis. Statistically significant association was observed between increasing statins intake and liver cancer risk reduction (OR = 0.46, 95%CI: 0.24-0.68, P risk of liver cancer for an increase of 50 cumulative defined daily dose per year was 0.86 (95%CI: 0.81-0.90, P risk was found (P for nonlinearity analysis indicated that statins intake was associated with a significantly risk of liver cancer risk reduction in Asia (OR = 0.44, 95%CI: 0.11-0.77, P risk reduction.

  14. Data Mining: A Hybrid Methodology for Complex and Dynamic Research

    Science.gov (United States)

    Lang, Susan; Baehr, Craig

    2012-01-01

    This article provides an overview of the ways in which data and text mining have potential as research methodologies in composition studies. It introduces data mining in the context of the field of composition studies and discusses ways in which this methodology can complement and extend our existing research practices by blending the best of what…

  15. Does Googling lead to statin intolerance?

    Science.gov (United States)

    Khan, Sarah; Holbrook, Anne; Shah, Baiju R

    2018-07-01

    The nocebo effect, where patients with expectations of adverse effects are more likely to experience them, may contribute to the high rate of statin intolerance found in observational studies. Information that patients read on the internet may be a precipitant of this effect. The objective of the study was to establish whether the number of websites about statin side effects found using Google is associated with the prevalence of statin intolerance. The prevalence of statin intolerance in 13 countries across 5 continents was established in a recent study via a web-based survey of primary care physicians and specialists. Using the Google search engine for each country, the number of websites about statin side effects was determined, and standardized to the number of websites about statins overall. Searches were restricted to pages in the native language, and were conducted after connecting to each country using a virtual private network (VPN). English-speaking countries (Australia, Canada, UK, USA) had the highest prevalence of statin intolerance and also had the largest standardized number of websites about statin side effects. The sample Pearson correlation coefficient between these two variables was 0.868. Countries where patients using Google are more likely to find websites about statin side effects have greater levels of statin intolerance. The nocebo effect driven by online information may be contributing to statin intolerance. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. Model Validation and Verification of Data Mining from the ...

    African Journals Online (AJOL)

    In this paper, we seek to present a hybrid method for Model Validation and Verification of Data Mining from the Knowledge Workers Productivity Approach. It is hoped that this paper will help managers to implement different corresponding measures. A case study is presented where this model measure and validates at the ...

  17. The multiple zeta value data mine

    Energy Technology Data Exchange (ETDEWEB)

    Buemlein, J. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Broadhurst, D.J. [Open Univ., Milton Keynes (United Kingdom). Physics and Astronomy Dept.; Vermaseren, J.A.M. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); NIKHEF, Amsterdam (Netherlands)

    2009-07-15

    We provide a data mine of proven results for multiple zeta values (MZVs) of the form {zeta}(s{sub 1},s{sub 2},..,s{sub k}) = sum {sup {infinity}}{sub n{sub 1}}{sub >n{sub 2}}{sub >...>n{sub k}}{sub >0} {l_brace}1/(n{sub 1}{sup s{sub 1}}..n{sub k}{sup s{sub k}}){r_brace} with weight w = sum {sup K}{sub i=1}s{sub i} and depth k and for Euler sums of the form sum {sup {infinity}}{sub n{sub 1}}{sub >n{sub 2}}{sub >...>n{sub k}}{sub >0} {l_brace}({epsilon}{sub 1}{sup n{sub 1}}..{epsilon}{sub 1}{sup n{sub k}})/(n{sub 1}{sup s{sub 1}}..n{sub k}{sup s{sub k}}){r_brace} with signs {epsilon}{sub i} = {+-} 1. Notably, we achieve explicit proven reductions of all MZVs with weights w{<=}22, and all Euler sums with weights w{<=}12, to bases whose dimensions, bigraded by weight and depth, have sizes in precise agreement with the Broadhurst. Kreimer and Broadhurst conjectures. Moreover, we lend further support to these conjectures by studying even greater weights (w{<=}30), using modular arithmetic. To obtain these results we derive a new type of relation for Euler sums, the Generalized Doubling Relations. We elucidate the ''pushdown'' mechanism, whereby the ornate enumeration of primitive MZVs, by weight and depth, is reconciled with the far simpler enumeration of primitive Euler sums. There is some evidence that this pushdown mechanism finds its origin in doubling relations. We hope that our data mine, obtained by exploiting the unique power of the computer algebra language FORM, will enable the study of many more such consequences of the double-shuffle algebra of MZVs, and their Euler cousins, which are already the subject of keen interest, to practitioners of quantum field theory, and to mathematicians alike. (orig.)

  18. Data Mining and Machine Learning in Astronomy

    Science.gov (United States)

    Ball, Nicholas M.; Brunner, Robert J.

    We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

  19. Prevalence of dyslipidaemia in statin-treated patients in Ireland: Irish results of the DYSlipidaemia International Study (DYSIS).

    LENUS (Irish Health Repository)

    Horgan, S

    2012-02-01

    BACKGROUND: Statins are proven to reduce cardiovascular risk; however, substantial risk remains in patients on statin therapy. Persisting dyslipidaemia is likely to play a contributory role. AIM: To assess the prevalence of persisting lipid abnormalities in patients treated with statins. METHODS: DYSIS was a cross-sectional study of 22,063 patients in Europe and Canada. 900 Irish patients participated. All patients were >\\/= 45 years and treated with statins for >\\/= 3 months. Data were collected from the patients\\' records. ESC guidelines were used to classify risk and to define lipid levels. RESULTS: Mean age was 66.1 years with women representing 40.7%. 78.6% were high-risk patients; that is 53.9% with cardiovascular disease (CVD), 20.1% with diabetes and 15.9% with a SCORE risk >\\/= 5%. Total cholesterol was not at goal in 34.4% of all patients. LDL-C was elevated in 30.8% of all patients and in 30% at high risk. Low HDL-C was found in 34.7% of high-risk patients compared to 16.9% of patients with an ESC score <5%. In diabetics without CVD, low HDL-C and elevated TGs were found in 46 and 44.3%, respectively. CONCLUSIONS: Despite statin therapy, a significant number of patients have persistent dyslipidaemia. While LDL-C targets are suboptimal in three out of ten patients, the prevalence of low HDL-C and high TGs in high-risk patients is greater than one in three. A more integrated approach to the treatment of patients with dyslipidaemia is warranted. Clinical trials are needed to assess the impact of therapies that raise HDL-C and lower elevated TGs.

  20. The association of statin use with reduced incidence of venous thromboembolism: a population-based cohort study.

    Science.gov (United States)

    Lassila, Riitta; Jula, Antti; Pitkäniemi, Janne; Haukka, Jari

    2014-11-05

    Venous thromboembolism (VTE) continues to be a frequent medical emergency requiring rapid recognition so as to reach diagnosis and initiate anticoagulation therapy. The use of statins in addition to reducing the incidence of arterial thrombosis for decreasing the incidence and reoccurrence of VTE is reported. The aim of our study was to explore the association between statin usage and the incidence of new VTE at the population level during a 10-year follow-up. Population-based historic cohort. The Health 2000 Survey was based on a nationally representative sample. 8028 individuals aged 30 years or over in Finland. The primary end point event was the first ever hospitalisation due to one of the following causes: pulmonary embolism (International Classification of Diseases-10 I26), cerebral venous non-pyogenic thrombosis (I63.6), or venous thrombosis (I80.9-189). The preselected explanatory variables applied to the Poisson regression model were statin usage (no/yes) during follow-up (2000-2011) and several baseline data (age, sex; usage of blood glucose lowering drugs, vitamin K antagonists and antiplatelet agents). We observed 136 VTE events, the incidence of 1.72 (95% CI 1.44 to 2.04) per 1000 person-years. Current statin usage did not associate with the incidence of VTE according to the univariate model (rate ratio (RR) 0.93, 0.56 to 1.52), but when adjusted with baseline variables (age, sex, medications) the RR declined to 0.60 (0.36 to 1.00, p=0.04). Statin use offers protection against first ever VTE events and appears as a primary prevention tool in patients without anticoagulation or antiplatelet medication. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  1. Statins in the prevention of dementia and Alzheimer's disease: a meta-analysis of observational studies and an assessment of confounding.

    Science.gov (United States)

    Wong, William B; Lin, Vincent W; Boudreau, Denise; Devine, Emily Beth

    2013-04-01

    Studies demonstrate the potential for statins to prevent dementia and Alzheimer's disease (AD), but the evidence is inconclusive. Conduct a meta-analysis to estimate any benefit of statins in preventing dementia and examine the potential effect of study design and confounding on the benefit of statins in dementia. A secondary goal is to explore factors that may elucidate the mechanisms by which statins exert their potentially beneficial effect. Performed systematic literature review to identify relevant publications. Relative risk (RR) estimates were pooled using both fixed and random effect models. Studies were stratified by study design and potential confounding factors. The pooled results for all-type dementia suggest that use of statins is associated with a lower RR of dementia when compared to non-statin users (random effects model: RR 0.82 (95%CI [0.69, 0.97]). The pooled results for AD also suggested a lower RR with statin user compared to non-statin users in random effects models (RR: 0.70, 95% CI [0.60, 0.83]). Study design and methods used to address biases may influence the results. These pooled results suggest that statins may provide a slight benefit in the prevention of AD and all-type dementia. This benefit observed in both disease states should be interpreted with caution as observational studies are subject to bias, and it is possible that the slight benefit observed may disappear when these biases are addressed in a well-designed randomized controlled trial. Copyright © 2012 John Wiley & Sons, Ltd.

  2. A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib.

    Directory of Open Access Journals (Sweden)

    David G Covell

    Full Text Available A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000 set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300 set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.

  3. Applying data mining techniques to improve diagnosis in neonatal jaundice

    Directory of Open Access Journals (Sweden)

    Ferreira Duarte

    2012-12-01

    Full Text Available Abstract Background Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies. Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. Methods This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology. This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa – EPE, from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer. Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48 and neural networks (multilayer perceptron. The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. Results The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. Conclusions The findings of our study sustain that, new approaches, such as data mining, may support

  4. Statins and the risk of acute pancreatitis: A population-based case-control study

    DEFF Research Database (Denmark)

    Thisted, Henriette; Jacobsen, Jacob; Munk, Estrid Muff

    2006-01-01

    , and 25 817 age- and gender-matched controls from the general population. Prescriptions for statins prior to admission with acute pancreatitis or index date among controls were retrieved from prescription databases. We used conditional logistic regression analysis to estimate odds ratios for acute......: Our findings speak against a strong causative effect of statins on the risk of acute pancreatitis, and may even indicate a mild protective effect....

  5. Data Mining Techniques for Customer Relationship Management

    Science.gov (United States)

    Guo, Feng; Qin, Huilin

    2017-10-01

    Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.

  6. [In vitro study over statins effects on cellular growth curves and its reversibility with mevalonate].

    Science.gov (United States)

    Millan Núñez-Cortés, Jesús; Alvarez Rodriguez, Ysmael; Alvarez Novés, Granada; Recarte Garcia-Andrade, Carlos; Alvarez-Sala Walther, Luis

    2014-01-01

    HMG-CoA-Reductase inhibitors, also known as statins, are currently the most powerful cholesterol-lowering drugs available on the market. Clinical trials and experimental evidence suggest that statins have heavy anti-atherosclerotic effects. These are in part consequence of lipid lowering but also result from pleiotropic actions of the drugs. These so-called pleiotropic properties affect various aspects of cell function, inflammation, coagulation, and vasomotor activity. These effects are mediated either indirectly through LDL-c reduction or via a direct effect on cellular functions. Although many of the pleiotropic properties of statins may be a class effect, some may be unique to certain agents and account for differences in their pharmacological activity. So, although statins typically have similar effects on LDL-c levels, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects. In this paper we analize the in vitro effects of different statins over different cell lines from cells implicated in atherosclerotic process: endothelial cells, fibroblasts, and vascular muscular cells. In relation with our results we can proof that the effects of different dosis of different statins provides singular effects over growth curves of different cellular lines, a despite of a class-dependent effects. So, pleiotropic effects and its reversibility with mevalonate are different according with the molecule and the dosis. Copyright © 2013 Elsevier España, S.L. y SEA. All rights reserved.

  7. Patients' perspectives on statin therapy for treatment of hypercholesterolaemia: a qualitative study.

    Science.gov (United States)

    Tolmie, Elizabeth P; Lindsay, Grace M; Kerr, Susan M; Brown, Malcom R; Ford, Ian; Gaw, Allan

    2003-07-01

    Health Care Practitioners' attempts to implement secondary prevention targets for coronary heart disease (CHD) may be restricted by low rates of persistence with statin therapy. There is a need to understand why some patients, despite having established CHD and elevated cholesterol, do not comply with their prescribed statin regimen. To explore patients' perspectives on compliance with statin therapy. Primary care, West of Scotland. The research approach was qualitative. Thirty-three patients prescribed statin therapy and identified as having different patterns of compliance (poor moderate and good) were interviewed. The in-depth interviews were conducted on a one to one basis. Patients prescribed statin therapy for less than three months were excluded. Data were analysed thematically with the assistance of QSR Nudist. From analysis of the narrative data, two broad categories, i.e. 'Patient-health care provider communication' and 'Health beliefs' were identified. These categories encompassed six main themes: 'Initiation of therapy'; 'Subsequent feedback'; 'Sources of misconceptions'; 'Unconditional acceptance'; 'Conditional acceptance'; 'Deferment and Rejection'. Acceptance of and compliance with statin therapy appeared to be associated with the provision, interpretation and feedback of information during patient-practitioner consultations, and patients' beliefs about personal health status, cholesterol, and recommended cholesterol-lowering strategies. Patients' beliefs and understanding about cholesterol, and the role of cholesterol modifying strategies should be determined prior to the initiation of therapy and at appropriate intervals thereafter.

  8. The Top Ten Algorithms in Data Mining

    CERN Document Server

    Wu, Xindong

    2009-01-01

    From classification and clustering to statistical learning, association analysis, and link mining, this book covers the most important topics in data mining research. It presents the ten most influential algorithms used in the data mining community today. Each chapter provides a detailed description of the algorithm, a discussion of available software implementation, advanced topics, and exercises. With a simple data set, examples illustrate how each algorithm works and highlight the overall performance of each algorithm in a real-world application. Featuring contributions from leading researc

  9. Digital family histories for data mining.

    Science.gov (United States)

    Hoyt, Robert; Linnville, Steven; Chung, Hui-Min; Hutfless, Brent; Rice, Courtney

    2013-01-01

    As we move closer to ubiquitous electronic health records (EHRs), genetic, familial, and clinical information will need to be incorporated into EHRs as structured data that can be used for data mining and clinical decision support. While the Human Genome Project has produced new and exciting genomic data, the cost to sequence the human personal genome is high, and significant controversies regarding how to interpret genomic data exist. Many experts feel that the family history is a surrogate marker for genetic information and should be part of any paper-based or electronic health record. A digital family history is now part of the Meaningful Use Stage 2 menu objectives for EHR reimbursement, projected for 2014. In this study, a secure online family history questionnaire was designed to collect data on a unique cohort of Vietnam-era repatriated male veterans and a comparison group in order to compare participant and family disease rates on common medical disorders with a genetic component. This article describes our approach to create the digital questionnaire and the results of analyzing family history data on 319 male participants.

  10. Quantification of Operational Risk Using A Data Mining

    Science.gov (United States)

    Perera, J. Sebastian

    1999-01-01

    What is Data Mining? - Data Mining is the process of finding actionable information hidden in raw data. - Data Mining helps find hidden patterns, trends, and important relationships often buried in a sea of data - Typically, automated software tools based on advanced statistical analysis and data modeling technology can be utilized to automate the data mining process

  11. Associations between patients' risk attitude and their adherence to statin treatment - a population based questionnaire and register study

    DEFF Research Database (Denmark)

    Barfoed, Benedicte Marie Lind; Paulsen, Maja Skov; Christensen, Palle Mark

    2016-01-01

    -based study on a sample of 6393 persons of the general. Danish population aged 20-79. Data on risk attitude were based on 4 items uncovering health-related as well as financial dimensions of risk attitude. They were collected through a web-based questionnaire and combined with register data on redeemed statin...... the risk-averse patients, OR 0.80 (95 %-CI 0.68-0.95) and OR 0.83 (95 %-CI 0.71-0.98), respectively. No significant association was found between adherence and financial risk attitude. Further, patients in the youngest age group and patients with no CVD were less adherent to statin treatment. CONCLUSION......BACKGROUND: Poor adherence to medical treatment may have considerable consequences for the patients' health and for healthcare costs to society. The need to understand the determinants for poor adherence has motivated several studies on socio-demographics and comorbidity. Few studies focus...

  12. Comparative genomics using data mining tools

    Indian Academy of Sciences (India)

    Unknown

    1 | February 2002. Comparative genomics using data mining tools. 17 where L is the length of the concerned protein in amino acids and fi is the average frequency of occurrence of the ith amino acid in the set of proteins that are of high sequence complexity and are predicted to have globular fold within the same genome.

  13. Tools for Educational Data Mining: A Review

    Science.gov (United States)

    Slater, Stefan; Joksimovic, Srecko; Kovanovic, Vitomir; Baker, Ryan S.; Gasevic, Dragan

    2017-01-01

    In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will…

  14. Data Mining Gets Traction in Education

    Science.gov (United States)

    Sparks, Sarah D.

    2011-01-01

    The new and rapidly growing field of educational data mining is using the chaff from data collected through normal school activities to explore learning in more detail than ever, and researchers say the day when educators can make use of Amazon.com-like feedback on student learning behaviors may be closer than most people think. Educational data…

  15. Engaging Business Students with Data Mining

    Science.gov (United States)

    Brandon, Dan

    2016-01-01

    The Economist calls it "a golden vein", and many business experts now say it is the new science of winning. Business and technologists have many names for this new science, "business intelligence" (BI), " data analytics," and "data mining" are among the most common. The job market for people skilled in this…

  16. Traffic Flow Management: Data Mining Update

    Science.gov (United States)

    Grabbe, Shon R.

    2012-01-01

    This presentation provides an update on recent data mining efforts that have been designed to (1) identify like/similar days in the national airspace system, (2) cluster/aggregate national-level rerouting data and (3) apply machine learning techniques to predict when Ground Delay Programs are required at a weather-impacted airport

  17. Knowledge Discovery and Data Mining: An Overview

    Science.gov (United States)

    Fayyad, U.

    1995-01-01

    The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

  18. Data mining applications in healthcare | Ogwueleka | International ...

    African Journals Online (AJOL)

    Data mining applications have benefited the healthcare industry in terms of fraud and abuse detection by insurers, use in customer relationship management decisions by healthcare organizations and identification of effective treatments and best practices by physicians. The enormous data generated by healthcare ...

  19. Highly Robust Methods in Data Mining

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2013-01-01

    Roč. 8, č. 1 (2013), s. 9-24 ISSN 1452-4864 Institutional support: RVO:67985807 Keywords : data mining * robust statistics * high-dimensional data * cluster analysis * logistic regression * neural networks Subject RIV: BB - Applied Statistics, Operational Research

  20. Comparative genomics using data mining tools

    Indian Academy of Sciences (India)

    We have analysed the genomes of representatives of three kingdoms of life, namely, archaea, eubacteria and eukaryota using data mining tools based on compositional analyses of the protein sequences. The representatives chosen in this analysis were Methanococcus jannaschii, Haemophilus influenzae and ...

  1. Comparative genomics using data mining tools

    Indian Academy of Sciences (India)

    Unknown

    We have analysed the genomes of representatives of three kingdoms of life, namely, archaea, eubacteria and eukaryota using data mining tools based on compositional analyses of the protein sequences. The repre- sentatives chosen in this analysis were Methanococcus jannaschii, Haemophilus influenzae and ...

  2. Statin-related myotoxicity

    OpenAIRE

    Fernandes, V; Santos, MJ; Pérez, A

    2016-01-01

    Statin therapy has a very important role in decreasing cardiovascular risk, and treatment non-compliance may therefore be a concern in high cardiovascular risk patients. Myotoxicity is a frequent side effect of statin therapy and one of the main causes of statin discontinuation, which limits effective treatment of patients at risk of or with cardiovascular disease. Because of the high proportion of patients on statin treatment and the frequency of statin-related myotoxicity, this is a subject...

  3. A Framework for Investigating Influence of Organizational Decision Makers on Data Mining Process Achievement

    Directory of Open Access Journals (Sweden)

    Hanieh Hajisafari

    2012-02-01

    Full Text Available Currently, few studies deal with evaluation of data mining plans in context of solvng organizational problems. A successful data miner is searching to solve a fully defined business problem. To make the data mining (DM results actionable, the data miner must explain them to the business insider. The interaction process between the business insiders and data miners is actually a knowledge-sharing process. In this study through representing a framwork, influence of organizational decision makers on data mining process and results investigated. By investigating research literature, the critical success factors of data mining plans was identified and the role of organizational decision makers in each step of data mining was investigated.‌ Then, the conceptual framework of influence of organizational decision makers on data mining process achievement was designed. By getting expert opinions, the proposed framework was analyzed and evantually designed the final framework of influence of organizational decision makers on data mining process achievement. Analysis of experts opinions showed that by knowledge sharing of data ming results with decision makers, "learning", "action or internalization" and "enforcing/unlearning" will become as critical success factors. Also, results of examining importance of decision makers' feedback on data mining steps showed that getting feedback from decision makers could have most influence on "knowledge extraction and representing model" step and least on "data cleaning and preprocessing" step.

  4. Cholesterol suppresses antimicrobial effect of statins

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Haeri

    2015-12-01

    Full Text Available Objective(s:Isoprenoid biosynthesis is a key metabolic pathway to produce a wide variety of biomolecules such as cholesterol and carotenoids, which target cell membranes. On the other hand, it has been reported that statins known as inhibitors of isoprenoid biosynthesis and cholesterol lowering agents, may have a direct antimicrobial effect on the some bacteria. The exact action of statins in microbial metabolism is not clearly understood. It is possible that statins inhibit synthesis or utilization of some sterol precursor necessary for bacterial membrane integrity. Accordingly, this study was designed in order to examine if statins inhibit the production of a compound, which can be used in the membrane, and whether cholesterol would replace it and rescue bacteria from toxic effects of statins. Materials and Methods: To examine the possibility we assessed antibacterial effect of statins with different classes; lovastatin, simvastatin, and atorvastatin, alone and in combination with cholesterol on two Gram-positive (Staphylococcus aureus and Enterococcus faecalis and two Gram-negative (Pseudomonas aeruginosa and Escherichia coli bacteria using gel diffusion assay. Results: Our results showed that all of the statins except for lovastatin had significant antibacterial property in S. aureus, E. coli, and Enter. faecalis. Surprisingly, cholesterol nullified the antimicrobial action of effective statins in statin-sensitive bacteria. Conclusion: It is concluded that statins may deprive bacteria from a metabolite responsible for membrane stability, which is effectively substituted by cholesterol.

  5. Statin intolerance - a question of definition.

    Science.gov (United States)

    Algharably, Engi Abdel-Hady; Filler, Iris; Rosenfeld, Stephanie; Grabowski, Katja; Kreutz, Reinhold

    2017-01-01

    Statin therapy is the backbone of pharmacologic therapy for low-density lipoproteins cholesterol lowering and plays a pivotal role in cardiovascular disease prevention. Statin intolerance is understood as the inability to continue using a statin to reduce individual cardiovascular risk sufficiently, due to the development of symptoms or laboratory abnormalities attributable to the initiation or dose escalation of a statin. Muscle symptoms are the most common side effects observed. Areas covered: The main aim of this article is to present a review on published definitions of statin intolerance. In addition, a brief review on clinical aspects and risk factors of statin intolerance is provided and features for a common definition for statin intolerance are suggested. Expert opinion: A definition of statin intolerance by major drug regulatory agencies is not available. In clinical studies, different definitions are chosen and results are not comparable; different medical associations do not agree on one common definition. There is an unmet need to establish a common definition of statin intolerance to ensure an appropriate clinical use of this important drug class. Further work is required to develop a consensus definition on statin intolerance that could have significant positive impact on both research and clinical management.

  6. Time-to-signal comparison for drug safety data-mining algorithms vs. traditional signaling criteria.

    Science.gov (United States)

    Hochberg, A M; Hauben, M

    2009-06-01

    Data mining may improve identification of signals, but its incremental utility is in question. The objective of this study was to compare associations highlighted by data mining vs. those highlighted through the use of traditional decision rules. In the case of 29 drugs, we used US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) data to compare three data-mining algorithms (DMAs) with two traditional decision rules: (i) N >or= 3 reports for a designated medical event (DME) and (ii) any event comprising >2% of reports in relation to a drug. Data-mining methods produced 101-324 signals vs. 1,051 for the N >or= 3 rule but yielded a higher proportion of signals having publication support. For the 2% rule, the fraction of signals having publication support was similar to that associated with data mining. Data-mining signals lagged N >or= 3 signaling by 1.5-11.0 months. It may therefore be concluded that data mining identifies fewer signals than the "N >or= 3 DME" rule. The signals appear later with data mining but are more often supported by publications. In the case of the 2% rule, no such difference in publication support was observed.

  7. Statins in acute coronary syndromes.

    Science.gov (United States)

    Sposito, Alexandre Russo; Aguiar Filho, Gentil Barreira de; Aarão, Amanda Rezende; Sousa, Francisco Thiago Tomaz de; Bertolami, Marcelo Chiara

    2011-10-01

    Statins are the main resource available to reduce LDL-cholesterol levels. Their continuous use decreases cardiovascular morbidity and mortality due to atherosclerosis. The administration of these medications demonstrated to be effective in primary and secondary prevention clinical trials in low and high risk patients. Specialists believe that a possible benefit of hypolipidemic therapy in preventing complications of atherosclerotic diseases is in the reduction of deposition of atherogenic lipoproteins in vulnerable areas of the vasculature. Experimental studies with statins have shown an enormous variety of other effects that could extend the clinical benefit beyond the lipid profile modification itself. Statin-based therapies benefit other important components of the atherothrombotic process: inflammation, oxidation, coagulation, fibrinolysis, endothelial function, vasoreactivity and platelet function. The demonstration of the effects that do not depend on cholesterol lowering or the pleiotropic effects of statins provides the theoretical basis for their potential role as adjunctive therapy in acute coronary syndromes. Retrospective analyses of a variety of studies indicate the potential benefit of statins during acute coronary events. Recent clinical studies have addressed this important issue in prospective controlled trials showing strong evidence for the administration of statins as adjunctive therapy in acute coronary syndromes.

  8. Reference intervals data mining: no longer a probability paper method.

    Science.gov (United States)

    Katayev, Alexander; Fleming, James K; Luo, Dajie; Fisher, Arren H; Sharp, Thomas M

    2015-01-01

    To describe the application of a data-mining statistical algorithm for calculation of clinical laboratory tests reference intervals. Reference intervals for eight different analytes and different age and sex groups (a total of 11 separate reference intervals) for tests that are unlikely to be ordered during routine screening of disease-free populations were calculated using the modified algorithm for data mining of test results stored in the laboratory database and compared with published peer-reviewed studies that used direct sampling. The selection of analytes was based on the predefined criteria that include comparability of analytical methods with a statistically significant number of observations. Of the 11 calculated reference intervals, having upper and lower limits for each, 21 of 22 reference interval limits were not statistically different from the reference studies. The presented statistical algorithm is shown to be an accurate and practical tool for reference interval calculations. Copyright© by the American Society for Clinical Pathology.

  9. OCCUPATIONAL HEALTH AND SAFETY USING DATA MINING

    Directory of Open Access Journals (Sweden)

    Jelena Ruso

    2012-12-01

    Full Text Available Large amounts of the data gathered in organizations through business operations won’t have utility value unless they are used in a proper way. With growing amount of data, the issue of their storage, processing and analysis is becoming more complex. The proper data usage and analysis should provide guidance, solutions and the basis for predictions with the objective of improving and initiating future smart decisions based on the acquired results. Data mining is the tool which exactly enables discovering of emerging patterns and important business information. This work presents the example of Data Mining implementation in the field of workplace health, safety and welfare at HIP- Petrohemija, in Pančevo, as well as various approaches of data analysis and processing by various authors in this field.

  10. Understanding Genetic Toxicity Through Data Mining: The ...

    Science.gov (United States)

    This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies. This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies.

  11. Arabic Question Answering System Based On Data Mining

    Directory of Open Access Journals (Sweden)

    Waheeb Ahmed

    2017-02-01

    Full Text Available In this study we describe An Arabic Question AnsweringQA system based on data mining approach. The system employs text mining techniques to determine the likely answers to factoid questions. It depends mainly on the use of lexical information and does not apply any complex language processing tools such as named entity recognizers parsers and ontologies. The system achieved an accuracy of 61.5.

  12. Compound Data Mining for Drug Discovery.

    Science.gov (United States)

    Bajorath, Jürgen

    2017-01-01

    In recent years, there has been unprecedented growth in compound activity data in the public domain. These compound data provide an indispensable resource for drug discovery in academic environments as well as in the pharmaceutical industry. To handle large volumes of heterogeneous and complex compound data and extract discovery-relevant knowledge from these data, advanced computational mining approaches are required. Herein, major public compound data repositories are introduced, data confidence criteria reviewed, and selected data mining approaches discussed.

  13. Logics for Data Mining (GUHA Rediviva)

    Czech Academy of Sciences Publication Activity Database

    Hájek, Petr

    2000-01-01

    Roč. 10, č. 3 (2000), s. 301-311 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] R&D Projects: GA ČR GA201/00/1489; GA AV ČR IAA1030601 Institutional research plan: AV0Z1030915 Keywords : data mining * fuzzy logic * monadic calculi Subject RIV: BA - General Mathematics

  14. Solar Data Mining at Georgia State University

    Science.gov (United States)

    Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

    2016-12-01

    In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.

  15. Data mining for wind power forecasting

    OpenAIRE

    Fugon, Lionel; Juban, Jérémie; Kariniotakis, Georges

    2008-01-01

    International audience; Short-term forecasting of wind energy production up to 2-3 days ahead is recognized as a major contribution for reliable large-scale wind power integration. Increasing the value of wind generation through the improvement of prediction systems performance is recognised as one of the priorities in wind energy research needs for the coming years. This paper aims to evaluate Data Mining type of models for wind power forecasting. Models that are examined include neural netw...

  16. A Data Mining Approach to Intelligence Operations

    DEFF Research Database (Denmark)

    Memon, Nasrullah; Hicks, David; Harkiolakis, Nicholas

    2008-01-01

    agencies.   An emphasis in the paper is placed on Social Network Analysis and Investigative Data Mining, and the use of these technologies in the counterterrorism domain.  Tools and techniques from both areas are described, along with the important tasks for which they can be used to assist...... with the investigation and analysis of terrorist organizations.  The process of collecting data about these organizations is also considered along with the inherent difficulties that are involved....

  17. Automated detection of hereditary syndromes using data mining.

    Science.gov (United States)

    Evans, S; Lemon, S J; Deters, C A; Fusaro, R M; Lynch, H T

    1997-10-01

    Computer-based data mining methodology applied to family history clinical data can algorithmically create highly accurate, clinically oriented hereditary disease pattern recognizers. For the example of hereditary colon cancer, the data mining's selection of relevant factors to assess for hereditary colon cancer was statistically significant (P recognizer-formulated patterns of hereditary colon cancer were independently confirmed by a clinical expert. Applied to previously analyzed family histories, the recognizer identified the definitive hereditary histories, correctly responded negatively to the putative hereditary histories, and correctly responded negatively to empirically elevated colon cancer risk situations. This capability facilitates patient selection for DNA studies in search of gene mutations. When genetic mutations are included as parameters in a patient database for a genetic disease, the process yields an expert system which characterizes variations in clinical disease presentations in terms of genetic mutations. Such information can greatly improve the efficiency of gene testing.

  18. A Data Mining Approach for Cardiovascular Diagnosis

    Directory of Open Access Journals (Sweden)

    Pereira Joana

    2017-12-01

    Full Text Available The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

  19. Time Dependent Data Mining in RAVEN

    Energy Technology Data Exchange (ETDEWEB)

    Cogliati, Joshua Joseph [Idaho National Lab. (INL), Idaho Falls, ID (United States); Chen, Jun [Idaho National Lab. (INL), Idaho Falls, ID (United States); Patel, Japan Ketan [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Daniel Patrick [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Talbot, Paul William [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2016-09-01

    RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The goal of this type of analyses is to understand the response of such systems in particular with respect their probabilistic behavior, to understand their predictability and drivers or lack of thereof. Data mining capabilities are the cornerstones to perform such deep learning of system responses. For this reason static data mining capabilities were added last fiscal year (FY 15). In real applications, when dealing with complex multi-scale, multi-physics systems it seems natural that, during transients, the relevance of the different scales, and physics, would evolve over time. For these reasons the data mining capabilities have been extended allowing their application over time. In this writing it is reported a description of the new RAVEN capabilities implemented with several simple analytical tests to explain their application and highlight the proper implementation. The report concludes with the application of those newly implemented capabilities to the analysis of a simulation performed with the Bison code.

  20. Educational data mining applications and trends

    CERN Document Server

    2014-01-01

    This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research.  After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: ·     Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. ·     Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the students academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. ·     As...

  1. HIGHLY ROBUST METHODS IN DATA MINING

    Directory of Open Access Journals (Sweden)

    Jan Kalina

    2013-05-01

    Full Text Available This paper is devoted to highly robust methods for information extraction from data, with a special attention paid to methods suitable for management applications. The sensitivity of availabledata mining methods to the presence of outlying measurements in the observed data is discussed as a major drawback of available data mining methods. The paper proposes several newhighly robustmethods for data mining, which are based on the idea of implicit weighting of individual data values.Particularly it propose a novel robust method of hierarchical cluster analysis, which is a popular data mining method of unsupervised learning. Further, a robust method for estimating parameters in thelogistic regression was proposed. This idea is extended to a robust multinomial logistic classification analysis. Finally, the sensitivity of neural networks to the presence of noise and outlying measurements in the data was discussed. The method for robust training of neural networks for the task of function approximation, which has the form of a robust estimator in nonlinear regression, was proposed.

  2. Associations Between Statin Use and Physical Function in Older Adults from The Netherlands and Australia: Longitudinal Aging Study Amsterdam and Australian Longitudinal Study on Women's Health.

    Science.gov (United States)

    van Boheemen, Laurette; Tett, Susan E; Sohl, Evelien; Hugtenburg, Jacqueline G; van Schoor, Natasja M; Peeters, G M E E

    2016-06-01

    Statin therapy may cause myopathy, but long-term effects on physical function are unclear. We investigated whether statin use is associated with poorer physical function in two population-based cohorts of older adults. Data were from 691 men and women (aged 69-102 years in 2005/2006) in the LASA (Longitudinal Aging Study Amsterdam) and 5912 women (aged 79-84 years in 2005) in the ALSWH (Australian Longitudinal Study on Women's Health). Statin use and dose were sourced from containers (LASA) and administrative databases (ALSWH). Physical function was assessed using performance tests, questionnaires on functional limitations and the SF-12 (LASA) and SF-36 (ALSWH) questionnaires. Cross-sectional (both studies) and 3-year prospective associations (ALSWH) were analysed for different statin dosage using linear and logistic regression. In total, 25 % of participants in LASA and 61 % in ALSWH used statins. In the cross-sectional models in LASA, statin users were less likely to have functional limitations (percentage of subjects with at least 1 limitation 63.9 vs. 64.2; odds ratio [OR] 0.6; 95 % confidence interval [CI] 0.3-0.9) and had better SF-12 physical component scores (mean [adjusted] 47.3 vs. 44.5; beta [B] = 2.8; 95 % CI 1.1-4.5); in ALSWH, statin users had better SF-36 physical component scores (mean [adjusted] 37.4 vs. 36.5; B = 0.9; 95 % CI 0.3-1.5) and physical functioning subscale scores (mean [adjusted] 55.1 vs. 52.6; B = 2.4; 95 % CI 1.1-3.8) than non-users. Similar associations were found for low- and high-dose users and in the prospective models. In contrast, no significant associations were found with performance tests. Two databases from longitudinal population studies in older adults gave comparable results, even though different outcome measures were used. In these two large cohorts, statin use was associated with better self-perceived physical function.

  3. Data mining application in customer relationship management for hospital inpatients.

    Science.gov (United States)

    Lee, Eun Whan

    2012-09-01

    This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM.

  4. Data Mining and Optimization Tools for Developing Engine Parameters Tools

    Science.gov (United States)

    Dhawan, Atam P.

    1998-01-01

    This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. Tricia Erhardt and I studied the problem domain for developing an Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy, datasets. From the study and discussion with NASA LeRC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of tile data for GA based multi-resolution optimal search.

  5. Data Mining Application in Customer Relationship Management for Hospital Inpatients

    Science.gov (United States)

    2012-01-01

    Objectives This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. Methods A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. Results Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. Conclusions To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM. PMID:23115740

  6. Statin use and vitreoretinal surgery: Findings from a Finnish population-based cohort study.

    Science.gov (United States)

    Loukovaara, Sirpa; Sahanne, Sari; Takala, Annika; Haukka, Jari

    2018-01-16

    Vitreoretinal (VR) surgery is the third most common intraocular surgery after refractive and cataract surgery. The impact of statin therapy on VR surgery outcomes remains unclear, despite a potentially beneficial effect. We explored the association of preoperative statin therapy and the need for revitrectomy after primary vitrectomy. Our historical, population-based, register-based, VR surgery cohort consisted of 5709 patients operated in a tertiary, academic referral hospital in Finland, during 2008-2014, covering 6.5 years. Subgroup analysis was performed as follows: eyes operated due to (i) rhegmatogenous retinal detachment (RRD), (ii) VR interface diseases (macular pucker/hole), (iii) diabetic maculopathy or proliferative retinopathy, (iv) vitreous haemorrhage, (v) lens subluxation, (vi) vitreous opacities or (vii) other VR indication. The primary end-point event was revitrectomy during a postoperative follow-up period of 1 year due to retinal redetachment, vitreous rehaemorrhage, postoperative endophthalmitis, recurrent pucker or unclosed macular hole. Rhegmatogenous retinal detachment (RRD) was the second most frequent indication of VR surgery, including 1916 patients, with 305 re-operations with rate 0.20 (95% CI 0.18-0.23) per person-year. Statin treatment in time of operation was associated with lower risk of re-operation according to relative scale (incidence rate ratio 0.72, 95% CI 0.53-0.97), but not in absolute scale (incidence rate difference -0.58, 95% CI -4.30 to 3.15 for 100 person-years). No association with statin therapy and vitrectomy outcome was observed in the other VR subgroups. Use of statin treatment was associated with a 28% lower risk of revitrectomy in patients operated due to RRD. Further randomized clinical trials are highly warranted. © 2018 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  7. Differential impact of statin on new-onset diabetes in different age groups: a population-based case-control study in women from an asian country.

    Directory of Open Access Journals (Sweden)

    Chih-Wei Chen

    Full Text Available BACKGROUND: Statins reduce cardiovascular risks but increase the risk of new-onset diabetes (NOD. The aim of this study is to determine what effect, if any, statins have on the risk of NOD events in a population-based case-control study. An evaluation of the relationship between age and statin-exposure on NOD risks was further examined in a female Asian population. METHOD: In a nationwide case-controlled study, the authors assessed 1065 female NOD patients and 10650 controls with matching ages, genders and physician visit dates. The impact of statin-exposure on NOD was examined through multiple logistic regression models. Subgroup analysis for exploring the risk of NOD and statin-exposure in different age groups was performed. RESULTS: Statin-exposure was statistically significantly associated with increased new-onset diabetes risks using multivariate analysis. Interaction effect between age and statin-exposure on NOD risk was noted. For atorvastatin, the risk of cDDDs>60 was highest among the 55-64 year-olds (adjusted odds ratio [OR], 8.0; 95% confidence interval [CI], 2.57-24.90. For rosuvastatin, the risk of cDDDs>60 was highest among the 40-54 year-olds (adjusted OR, 14.8; 95% CI, 2.27-96.15. For simvastatin, the risk of cDDDs>60 was highest among the 55-64 year-olds (adjusted OR, 15.8; 95% CI, 5.77-43.26. For pravastatin, the risk of cDDDs>60 was highest among the 55-64 year-olds (adjusted OR, 14.0; 95% CI, 1.56-125.18. CONCLUSIONS: This population-based study found that statin use is associated with an increased risk of NOD in women. The risk of statin-related NOD was more evident for women aged 40-64 years compared to women aged 65 or more, and was cumulative-dose dependent. The use of statins should always be determined by weighing the clinical benefits and potential risks for NOD, and the patients should be continuously monitored for adverse effects.

  8. An Overview on Data Mining of Nighttime Light Remote Sensing

    Directory of Open Access Journals (Sweden)

    LI Deren

    2015-06-01

    Full Text Available When observing the Earth from above at night, it is clear that the human settlement and major economic regions emit glorious light. At cloud-free nights, some remote sensing satellites can record visible radiance source, including city light, fishing boat light and fire, and these nighttime cloud-free images are remotely sensed nighttime light images. Different from daytime remote sensing, nighttime light remote sensing provides a unique perspective on human social activities, thus it has been widely used for spatial data mining of socioeconomic domains. Historically, researches on nighttime light remote sensing mostly focus on urban land cover and urban expansion mapping using DMSP/OLS imagery, but the nighttime light images are not the unique remote sensing source to do these works. Through decades of development of nighttime light product, the nighttime light remote sensing application has been extended to numerous interesting and scientific study domains such as econometrics, poverty estimation, light pollution, fishery and armed conflict. Among the application cases, it is surprising to see the Gross Domestic Production (GDP data can be corrected using the nighttime light data, and it is interesting to see mechanism of several diseases can be revealed by nighttime light images, while nighttime light are the unique remote sensing source to do the above works. As the nighttime light remote sensing has numerous applications, it is important to summarize the application of nighttime light remote sensing and its data mining fields. This paper introduced major satellite platform and sensors for observing nighttime light at first. Consequently, the paper summarized the progress of nighttime light remote sensing data mining in socioeconomic parameter estimation, urbanization monitoring, important event evaluation, environmental and healthy effects, fishery dynamic mapping, epidemiological research and natural gas flaring monitoring. Finally, future

  9. Statin intolerance in a referral lipid clinic.

    Science.gov (United States)

    Lakey, Wanda C; Greyshock, Nicole G; Kelley, Carly E; Siddiqui, Mohammad A; Ahmad, Umar; Lokhnygina, Yuliya V; Guyton, John R

    2016-01-01

    Statins effectively prevent atherosclerotic cardiovascular disease, but rates of statin discontinuation after adverse events are high. Describe the range and relative frequencies of adverse events potentially attributable to statins in lipid referral practice and assess statin rechallenge outcomes. Retrospective cohort study of 642 patients with statin-associated adverse events evaluated in a referral lipid clinic between January 1, 2004 and January 27, 2011. Patients experiencing adverse events by organ system included 92% with musculoskeletal, 8% central nervous system, 10% liver, 8% gastrointestinal, 5% peripheral nervous system, 5% skin, and 3% other events. Overlap of organ system involvement occurred in 22.5%. At least 1 follow-up visit was made by 557 patients, among whom overall median follow-up was 25 months. Among patients treated with a statin in the clinic, 71% remained on a statin at the last follow-up visit. Patients with hepatic transaminase increases by history were numerically more likely than the overall group to resume or remain on statin treatment, whereas those reporting central nervous system or gastrointestinal symptoms trended lower for statin maintenance. Among patients who experienced an adverse event after statin rechallenge, the majority (64%) were being treated with intermittent, nondaily dosing at the time of the adverse event. Although musculoskeletal symptoms are reported by 90% of patients with statin intolerance, symptoms involving other organ systems may be more frequent than previously supposed. Understanding the range of symptoms, time course, and impact on daily activities informs counseling in patient-centered practice, but assessment of causation by statins remains challenging. Published by Elsevier Inc.

  10. Power System Transient Stability Based on Data Mining Theory

    Science.gov (United States)

    Cui, Zhen; Shi, Jia; Wu, Runsheng; Lu, Dan; Cui, Mingde

    2018-01-01

    In order to study the stability of power system, a power system transient stability based on data mining theory is designed. By introducing association rules analysis in data mining theory, an association classification method for transient stability assessment is presented. A mathematical model of transient stability assessment based on data mining technology is established. Meanwhile, combining rule reasoning with classification prediction, the method of association classification is proposed to perform transient stability assessment. The transient stability index is used to identify the samples that cannot be correctly classified in association classification. Then, according to the critical stability of each sample, the time domain simulation method is used to determine the state, so as to ensure the accuracy of the final results. The results show that this stability assessment system can improve the speed of operation under the premise that the analysis result is completely correct, and the improved algorithm can find out the inherent relation between the change of power system operation mode and the change of transient stability degree.

  11. Statins Decrease Oxidative Stress and ICD Therapies

    Directory of Open Access Journals (Sweden)

    Heather L. Bloom

    2010-01-01

    Full Text Available Recent studies demonstrate that statins decrease ventricular arrhythmias in internal cardioverter defibrillator (ICD patients. The mechanism is unknown, but evidence links increased inflammatory and oxidative states with increased arrhythmias. We hypothesized that statin use decreases oxidation. Methods. 304 subjects with ICDs were surveyed for ventricular arrhythmia. Blood was analyzed for derivatives of reactive oxygen species (DROMs and interleukin-6 (IL-6. Results. Subjects included 252 (83% men, 58% on statins, 20% had ventricular arrhythmias. Average age was 63 years and ejection fraction (EF 20%. ICD implant duration was 29 ± 27 months. Use of statins correlated with lower ICD events (r=0.12, P=.02. Subjects on statins had lower hsCRP (5.2 versus 6.3; P=.05 and DROM levels (373 versus 397; P=.03. Other factors, including IL-6 and EF did not differ between statin and nonstatin use, nor did beta-blocker or antiarrhythmic use. Multivariate cross-correlation analysis demonstrated that DROMs, statins, IL-6 and EF were strongly associated with ICD events. Multivariate regression shows DROMs to be the dominant predictor. Conclusion. ICD event rate correlates with DROMs, a measure of lipid peroxides. Use of statins is associated with reduced DROMs and fewer ICD events, suggesting that statins exert their effect through reducing oxidation.

  12. Data Mining: Applications, tools, learning types and other subtopics

    Directory of Open Access Journals (Sweden)

    Deborah Ribeiro Carvalho

    2015-03-01

    Full Text Available Experts in the field of data mining present concepts, features, limitations and possibilities of the data mining process, including the indication of tools available, links to artificial intelligence, and the implications of it's use in business intelligence.

  13. Uncertainty modeling for data mining a label semantics approach

    CERN Document Server

    Qin, Zengchang

    2014-01-01

    Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts.

  14. Data Mining Based on Cloud-Computing Technology

    Directory of Open Access Journals (Sweden)

    Ren Ying

    2016-01-01

    Full Text Available There are performance bottlenecks and scalability problems when traditional data-mining system is used in cloud computing. In this paper, we present a data-mining platform based on cloud computing. Compared with a traditional data mining system, this platform is highly scalable, has massive data processing capacities, is service-oriented, and has low hardware cost. This platform can support the design and applications of a wide range of distributed data-mining systems.

  15. SIAM Data Mining Brings It’ to Annual Meeting

    Science.gov (United States)

    2017-02-24

    SIAM Data Mining "Brings It" to Annual Meeting Jeremy Kepner1 ( Data Mining SIAG Chair), Sanjukta Bhowmick2, Aydın Buluç3, Rajmonda Caceres1...Laboratory, 4Smith College, 5Pacific Northwest National Laboratory 1 The Data Mining Activity Group is one of SIAM’s most vibrant and dynamic...activity groups. To better share our enthusiasm for data mining with the broader SIAM community, our activity group organized six minisymposia at

  16. Impact of statins on risk of new onset diabetes mellitus: a population-based cohort study using the Korean National Health Insurance claims database

    Directory of Open Access Journals (Sweden)

    Lee J

    2016-10-01

    Full Text Available Jimin Lee,1 Yoojin Noh,1 Sooyoung Shin,1 Hong-Seok Lim,2 Rae Woong Park,3 Soo Kyung Bae,4 Euichaul Oh,4 Grace Juyun Kim,5 Ju Han Kim,5 Sukhyang Lee1 1Division of Clinical Pharmacy, College of Pharmacy, Ajou University, Suwon, South Korea; 2Department of Cardiology, School of Medicine, Ajou University, Suwon, South Korea; 3Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, South Korea; 4Division of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea; 5Division of Biomedical Informatics, College of Medicine, Seoul National University, Seoul, South Korea Abstract: Statin therapy is beneficial in reducing cardiovascular events and mortalities in patients with atherosclerotic cardiovascular diseases. Yet, there have been concerns of increased risk of diabetes with statin use. This study was aimed to evaluate the association between statins and new onset diabetes mellitus (NODM in patients with ischemic heart disease (IHD utilizing the Korean Health Insurance Review and Assessment Service claims database. Among adult patients with preexisting IHD, new statin users and matched nonstatin users were identified on a 1:1 ratio using proportionate stratified random sampling by sex and age. They were subsequently propensity score matched further with age and comorbidities to reduce the selection bias. Overall incidence rates, cumulative rates and hazard ratios (HRs between statin use and occurrence of NODM were estimated. The subgroup analyses were performed according to sex, age groups, and the individual agents and intensities of statins. A total of 156,360 patients (94,370 in the statin users and 61,990 in the nonstatin users were included in the analysis. The incidence rates of NODM were 7.8% and 4.8% in the statin users and nonstatin users, respectively. The risk of NODM was higher among statin users (crude HR 2.01, 95% confidence interval [CI] 1.93–2.10; adjusted HR 1

  17. 76 FR 14637 - State Medicaid Fraud Control Units; Data Mining

    Science.gov (United States)

    2011-03-17

    ...] State Medicaid Fraud Control Units; Data Mining AGENCY: Office of Inspector General (OIG), HHS. ACTION... and analyzing State Medicaid claims data, known as data mining. To support and modernize MFCU efforts... (FFP) in the costs of defined data mining activities under specified conditions. In addition, we...

  18. A Survey of Educational Data-Mining Research

    Science.gov (United States)

    Huebner, Richard A.

    2013-01-01

    Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…

  19. 78 FR 29055 - State Medicaid Fraud Control Units; Data Mining

    Science.gov (United States)

    2013-05-17

    ...] State Medicaid Fraud Control Units; Data Mining AGENCY: Office of Inspector General (OIG), HHS. ACTION... analyzing State Medicaid data, known as data mining. To support and modernize MFCU efforts to effectively... costs of defined data mining activities under specified circumstances. In addition, we finalize...

  20. Using Data Mining to Teach Applied Statistics and Correlation

    Science.gov (United States)

    Hartnett, Jessica L.

    2016-01-01

    This article describes two class activities that introduce the concept of data mining and very basic data mining analyses. Assessment data suggest that students learned some of the conceptual basics of data mining, understood some of the ethical concerns related to the practice, and were able to perform correlations via the Statistical Package for…

  1. Interestingness of association rules in data mining: Issues relevant ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    mated nature of data mining algorithms may result in a glut of patterns – the sheer numbers of which contribute to .... represented by data mining patterns is impeded by the 'glut' of patterns generated by data ..... (2000) have examined various measures proposed in statistics, machine learning and data mining literature.

  2. Data Mining and Knowledge Management in Higher Education -Potential Applications.

    Science.gov (United States)

    Luan, Jing

    This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

  3. Data mining, mining data : energy consumption modelling

    Energy Technology Data Exchange (ETDEWEB)

    Dessureault, S. [Arizona Univ., Tucson, AZ (United States)

    2007-09-15

    Most modern mining operations are accumulating large amounts of data on production and business processes. Data, however, provides value only if it can be translated into information that appropriate users can utilize. This paper emphasized that a new technological focus should emerge, notably how to concentrate data into information; analyze information sufficiently to become knowledge; and, act on that knowledge. Researchers at the Mining Information Systems and Operations Management (MISOM) laboratory at the University of Arizona have created a method to transform data into action. The data-to-action approach was exercised in the development of an energy consumption model (ECM), in partnership with a major US-based copper mining company, 2 software companies, and the MISOM laboratory. The approach begins by integrating several key data sources using data warehousing techniques, and increasing the existing level of integration and data cleaning. An online analytical processing (OLAP) cube was also created to investigate the data and identify a subset of several million records. Data mining algorithms were applied using the information that was isolated by the OLAP cube. The data mining results showed that traditional cost drivers of energy consumption are poor predictors. A comparison was made between traditional methods of predicting energy consumption and the prediction formed using data mining. Traditionally, in the mines for which data were available, monthly averages of tons and distance are used to predict diesel fuel consumption. However, this article showed that new information technology can be used to incorporate many more variables into the budgeting process, resulting in more accurate predictions. The ECM helped mine planners improve the prediction of energy use through more data integration, measure development, and workflow analysis. 5 refs., 11 figs.

  4. Management of statin intolerance

    Directory of Open Access Journals (Sweden)

    Soma B Raju

    2013-01-01

    Full Text Available Statins are the revolutionary drugs in the cardiovascular pharmacotherapy. But they also possess several adverse effects like myopathy with elevation of hepatic transaminases (>3 times the upper limit of normal or creatine kinase (>10 times the upper limit of normal and some rare side-effects, including peripheral neuropathy, memory loss, sleep disturbances, and erectile dysfunction. Due to these adverse effects, patients abruptly withdrew statins without consulting physicians. This abrupt discontinuation of statins is termed as statin intolerance. Statin-induced myopathy constitutes two third of all side-effects from statins and is the primary reason for statin intolerance. Though statin intolerance has considerably impacted cardiovascular outcomes in the high-risk patients, it has been well effectively managed by prescribing statins either as alternate-day or once weekly dosage regimen, as combination therapy with a non-statin therapy or and by dietary intervention. The present article reviews the causes, clinical implications of statin withdrawal and management of statin intolerance.

  5. Proactive data mining with decision trees

    CERN Document Server

    Dahan, Haim; Rokach, Lior; Maimon, Oded

    2014-01-01

    This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting crite

  6. Analyzing Log Files using Data-Mining

    Directory of Open Access Journals (Sweden)

    Marius Mihut

    2008-01-01

    Full Text Available Information systems (i.e. servers, applications and communication devices create a large amount of monitoring data that are saved as log files. For analyzing them, a data-mining approach is helpful. This article presents the steps which are necessary for creating an ‘analyzing instrument’, based on an open source software called Waikato Environment for Knowledge Analysis (Weka [1]. For exemplification, a system log file created by a Windows-based operating system, is used as input file.

  7. Patent data mining method and apparatus

    Science.gov (United States)

    Boyack, Kevin W.; Grafe, V. Gerald; Johnson, David K.; Wylie, Brian N.

    2002-01-01

    A method of data mining represents related patents in a multidimensional space. Distance between patents in the multidimensional space corresponds to the extent of relationship between the patents. The relationship between pairings of patents can be expressed based on weighted combinations of several predicates. The user can select portions of the space to perceive. The user also can interact with and control the communication of the space, focusing attention on aspects of the space of most interest. The multidimensional spatial representation allows more ready comprehension of the structure of the relationships among the patents.

  8. Multimedia data mining and analytics disruptive innovation

    CERN Document Server

    Baughman, Aaron; Pan, Jia-Yu; Petrushin, Valery A

    2015-01-01

    This authoritative text/reference provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. Presenting a detailed exploration into the progression of the field, the book describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications.

  9. Geographical variation in morphometry, craniometry, and diet of amammalian species (Stone marten, Martes foina) using data mining

    OpenAIRE

    PAPAKOSTA, MALAMATI; KITIKIDOU, KYRIAKI; BAKALOUDIS, DIMITRIOS; VLACHOS, CHRISTOS; CHATZINIKOS, EVANGELOS; ALEXANDROU, OLGA; SAKOULIS, ANASTASIOS

    2018-01-01

    Ecologists use various data mining techniques to make predictions and estimations, to identify patterns in datasets and relationships between qualitative and quantitative variables, or to classify variables. The aim of this study was to investigate if the application of data mining could be used to study geographical variation in the morphometry, craniometry, and diet of a mammalian species (Martes foina), and to determine whether data mining can complement genetic analysis to recognize subsp...

  10. The Weather Forecast Using Data Mining Research Based on Cloud Computing.

    Science.gov (United States)

    Wang, ZhanJie; Mazharul Mujib, A. B. M.

    2017-10-01

    Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

  11. Big data mining: In-database Oracle data mining over hadoop

    Science.gov (United States)

    Kovacheva, Zlatinka; Naydenova, Ina; Kaloyanova, Kalinka; Markov, Krasimir

    2017-07-01

    Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

  12. Statins Reduces the Risk of Dementia in Patients with Late-Onset Depression: A Retrospective Cohort Study.

    Directory of Open Access Journals (Sweden)

    Ya-Hsu Yang

    Full Text Available Patients with late-onset depression (LOD have been reported to run a higher risk of subsequent dementia. The present study was conducted to assess whether statins can reduce the risk of dementia in these patients.We used the data from National Health Insurance of Taiwan during 1996-2009. Standardized Incidence Ratios (SIRs were calculated for LOD and subsequent dementia. The criteria for LOD diagnoses included age ≥65 years, diagnosis of depression after 65 years of age, at least three service claims, and treatment with antidepressants. The time-dependent Cox proportional hazards model was applied for multivariate analyses. Propensity scores with the one-to-one nearest-neighbor matching model were used to select matching patients for validation studies. Kaplan-Meier curve estimate was used to measure the group of patients with dementia living after diagnosis of LOD.Totally 45,973 patients aged ≥65 years were enrolled. The prevalence of LOD was 12.9% (5,952/45,973. Patients with LOD showed to have a higher incidence of subsequent dementia compared with those without LOD (Odds Ratio: 2.785; 95% CI 2.619-2.958. Among patients with LOD, lipid lowering agent (LLA users (for at least 3 months had lower incidence of subsequent dementia than non-users (Hazard Ratio = 0.781, 95% CI 0.685-0.891. Nevertheless, only statins users showed to have reduced risk of dementia (Hazard Ratio = 0.674, 95% CI 0.547-0.832 while other LLAs did not, which was further validated by Kaplan-Meier estimates after we used the propensity scores with the one-to-one nearest-neighbor matching model to control the confounding factors.Statins may reduce the risk of subsequent dementia in patients with LOD.

  13. Optimal sampling strategy for data mining

    International Nuclear Information System (INIS)

    Ghaffar, A.; Shahbaz, M.; Mahmood, W.

    2013-01-01

    Latest technology like Internet, corporate intranets, data warehouses, ERP's, satellites, digital sensors, embedded systems, mobiles networks all are generating such a massive amount of data that it is getting very difficult to analyze and understand all these data, even using data mining tools. Huge datasets are becoming a difficult challenge for classification algorithms. With increasing amounts of data, data mining algorithms are getting slower and analysis is getting less interactive. Sampling can be a solution. Using a fraction of computing resources, Sampling can often provide same level of accuracy. The process of sampling requires much care because there are many factors involved in the determination of correct sample size. The approach proposed in this paper tries to find a solution to this problem. Based on a statistical formula, after setting some parameters, it returns a sample size called s ufficient sample size , which is then selected through probability sampling. Results indicate the usefulness of this technique in coping with the problem of huge datasets. (author)

  14. Data mining for multiwavelength cross-referencing

    Science.gov (United States)

    Voisin, Bruno; Donas, Jose

    2001-11-01

    In this paper, we deal with FOCA ultraviolet data and their cross-referencing with the DPOSS optical catalog, through data mining techniques. While traditional cross-referencing consists in correcting catalog coordinates in order to seek nearest candidate, non-optical surveys tend to have lower resolutions and more coordinates uncertainties. Then, it seemed to be a loss not to use more light sources parameters obtained through image processing pipelines. A data mining approach based on decision trees (machine learning algorithms), we processed different FOCA/DPOSS sources pairs that we could suppose being the same stellar entity, and some other pairs, obviously too distant to match. Trees use every existing ultraviolet/optical parameter present on catalog, excluding only coordinates. The resulting trees allows a classification of any FOCA/DPOSS pair, giving a probability for the pair to match, i.e. come from the same source. The originality of this method is the use of non-position parameters, that can be used for cross-referencing various catalogs in different wavelength without the need to homogenize coordinates systems. Such methods could be tools for working on upcoming multi-wavelength catalogs.

  15. Asymmetric threat data mining and knowledge discovery

    Science.gov (United States)

    Gilmore, John F.; Pagels, Michael A.; Palk, Justin

    2001-03-01

    Asymmetric threats differ from the conventional force-on- force military encounters that the Defense Department has historically been trained to engage. Terrorism by its nature is now an operational activity that is neither easily detected or countered as its very existence depends on small covert attacks exploiting the element of surprise. But terrorism does have defined forms, motivations, tactics and organizational structure. Exploiting a terrorism taxonomy provides the opportunity to discover and assess knowledge of terrorist operations. This paper describes the Asymmetric Threat Terrorist Assessment, Countering, and Knowledge (ATTACK) system. ATTACK has been developed to (a) data mine open source intelligence (OSINT) information from web-based newspaper sources, video news web casts, and actual terrorist web sites, (b) evaluate this information against a terrorism taxonomy, (c) exploit country/region specific social, economic, political, and religious knowledge, and (d) discover and predict potential terrorist activities and association links. Details of the asymmetric threat structure and the ATTACK system architecture are presented with results of an actual terrorist data mining and knowledge discovery test case shown.

  16. Data mining: childhood injury control and beyond.

    Science.gov (United States)

    Tepas, Joseph J

    2009-08-01

    Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or data sets. It has become a major part of modern life and is extensively used in industry, banking, government, and health care delivery. The process requires a data collection system that integrates input from multiple sources containing critical elements that define outcomes of interest. Appropriately designed data mining processes identify and adjust for confounding variables. The statistical modeling used to manipulate accumulated data may involve any number of techniques. As predicted results are periodically analyzed against those observed, the model is consistently refined to optimize precision and accuracy. Whether applying integrated sources of clinical data to inferential probabilistic prediction of risk of ventilator-associated pneumonia or population surveillance for signs of bioterrorism, it is essential that modern health care providers have at least a rudimentary understanding of what the concept means, how it basically works, and what it means to current and future health care.

  17. Quantifying the contribution of statins to the decline in population mean cholesterol by socioeconomic group in England 1991 - 2012: a modelling study.

    Directory of Open Access Journals (Sweden)

    Chris Kypridemos

    Full Text Available Serum total cholesterol is one of the major targets for cardiovascular disease prevention. Statins are effective for cholesterol control in individual patients. At the population level, however, their contribution to total cholesterol decline remains unclear. The aim of this study was to quantify the contribution of statins to the observed fall in population mean cholesterol levels in England over the past two decades, and explore any differences between socioeconomic groups.This is a modelling study based on data from the Health Survey for England. We analysed changes in observed mean total cholesterol levels in the adult England population between 1991-92 (baseline and 2011-12. We then compared the observed changes with a counterfactual 'no statins' scenario, where the impact of statins on population total cholesterol was estimated and removed. We estimated uncertainty intervals (UI using Monte Carlo simulation, where confidence intervals (CI were impractical. In 2011-12, 13.2% (95% CI: 12.5-14.0% of the English adult population used statins at least once per week, compared with 1991-92 when the proportion was just 0.5% (95% CI: 0.3-1.0%. Between 1991-92 and 2011-12, mean total cholesterol declined from 5.86 mmol/L (95% CI: 5.82-5.90 to 5.17 mmol/L (95% CI: 5.14-5.20. For 2011-12, mean total cholesterol was lower in more deprived groups. In our 'no statins' scenario we predicted a mean total cholesterol of 5.36 mmol/L (95% CI: 5.33-5.40 for 2011-12. Statins were responsible for approximately 33.7% (95% UI: 28.9-38.8% of the total cholesterol reduction since 1991-92. The statin contribution to cholesterol reduction was greater among the more deprived groups of women, while showing little socio-economic gradient among men.Our model suggests that statins explained around a third of the substantial falls in total cholesterol observed in England since 1991. Approximately two thirds of the cholesterol decrease can reasonably be attributed non

  18. DECISION SUPPORT SYSTEM TO SUPPORT DECISION PROCESSES WITH DATA MINING

    Directory of Open Access Journals (Sweden)

    Rok Rupnik

    2007-06-01

    Full Text Available Traditional techniques of data analysis do not enable the solution of all kind of problems and for that reason they have become insufficient. This caused a newinterdisciplinary field of data mining to arise, encompassing both classical statistical, and modern machine learning techniques to support the data analysis and knowledge discovery from data. Data mining methods are powerful in dealing with large quantities of data, but on the other hand they are difficult to master by business users to facilitate decision support. In this paper we introduce our approach to integration of decision support system with data mining. We discuss the role of data mining to facilitate decision support, the use of data mining methods in decision support systems, discuss applied approaches and introduce a data mining decision support system called DMDSS - Data Mining Decision Support System. We also present some obtained results and plans for future development.

  19. Management Strategies for Statin-Associated Muscle Symptoms: How Useful Is Same-Statin Rechallenge?

    Science.gov (United States)

    Brennan, Emily T; Joy, Tisha R

    2017-05-01

    Statin-associated muscle symptoms (SAMS) are common. Rechallenge with the same statin (same-statin rechallenge) has recently been included as part of a proposed scoring index for diagnosing SAMS, but data regarding tolerability and efficacy of same-statin rechallenge, compared with other strategies, is minimal. In this study we evaluated the tolerability, percent change in low-density lipoprotein cholesterol (LDL-C), and proportion of patients achieving their LDL-C targets among 3 common management strategies-same-statin rechallenge, switching to a different statin (statin switch), and use of nonstatin medications only. We performed a retrospective analysis of 118 patients referred to our tertiary care centre for management of SAMS, defined as development of muscle-related symptoms with 2 or more statins. Baseline and last follow-up lipid parameters were documented. Patients were classified as tolerant of a strategy if, at their last follow-up, they remained on that strategy. After a median follow-up of 17 months, most (n = 79; 67%) patients were able to tolerate a statin. Tolerability was similar among the 3 treatment strategies (71% same-statin rechallenge vs 53% statin switch vs 57% for nonstatin therapy only; P = 0.11). Those in the same-statin rechallenge and statin switch groups achieved greater LDL-C reductions compared with those who only tolerated nonstatins (-38.8 ± 3.4% vs -36.4 ± 2.9% vs -17.3 ± 4.5%; P = 0.0007). A greater proportion of patients in the same-statin rechallenge group achieved their target LDL-C compared with those in the nonstatin therapy only group (50% vs 15%; odds ratio, 6.8; 95% confidence interval, 1.5-40.7; P = 0.04). Among individuals with a history of SAMS, most will tolerate statin therapy. Same-statin rechallenge was highly tolerable and efficacious. Thus, same-statin rechallenge might warrant increased utilization. Copyright © 2017 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

  20. Data Mining Activities for Bone Discipline - Current Status

    Science.gov (United States)

    Sibonga, J. D.; Pietrzyk, R. A.; Johnston, S. L.; Arnaud, S. B.

    2008-01-01

    The disciplinary goals of the Human Research Program are broadly discussed. There is a critical need to identify gaps in the evidence that would substantiate a skeletal health risk during and after spaceflight missions. As a result, data mining activities will be engaged to gather reviews of medical data and flight analog data and to propose additional measures and specific analyses. Several studies are briefly reviewed which have topics that partially address these gaps in knowledge, including bone strength recovery with recovery of bone mass density, current renal stone formation knowledge, herniated discs, and a review of bed rest studies conducted at Ames Human Research Facility.

  1. Data Mining Methods to Generate Severe Wind Gust Models

    Directory of Open Access Journals (Sweden)

    Subana Shanmuganathan

    2014-01-01

    Full Text Available Gaining knowledge on weather patterns, trends and the influence of their extremes on various crop production yields and quality continues to be a quest by scientists, agriculturists, and managers. Precise and timely information aids decision-making, which is widely accepted as intrinsically necessary for increased production and improved quality. Studies in this research domain, especially those related to data mining and interpretation are being carried out by the authors and their colleagues. Some of this work that relates to data definition, description, analysis, and modelling is described in this paper. This includes studies that have evaluated extreme dry/wet weather events against reported yield at different scales in general. They indicate the effects of weather extremes such as prolonged high temperatures, heavy rainfall, and severe wind gusts. Occurrences of these events are among the main weather extremes that impact on many crops worldwide. Wind gusts are difficult to anticipate due to their rapid manifestation and yet can have catastrophic effects on crops and buildings. This paper examines the use of data mining methods to reveal patterns in the weather conditions, such as time of the day, month of the year, wind direction, speed, and severity using a data set from a single location. Case study data is used to provide examples of how the methods used can elicit meaningful information and depict it in a fashion usable for management decision making. Historical weather data acquired between 2008 and 2012 has been used for this study from telemetry devices installed in a vineyard in the north of New Zealand. The results show that using data mining techniques and the local weather conditions, such as relative pressure, temperature, wind direction and speed recorded at irregular intervals, can produce new knowledge relating to wind gust patterns for vineyard management decision making.

  2. Pleiotropic effects of statins in stroke prevention

    Directory of Open Access Journals (Sweden)

    Yenny

    2009-08-01

    Full Text Available Cardiovascular disease is the leading cause of death and disability, and contributes substantially to healthcare budgets. The lipid-lowering drugs, 3-hydroxy-3-methylgulutaryl-coenzyme A (HMG-CoA reductase inhibitor or statins, reducing mortality and cardiovascular morbidity in patients with established cardiovascular disease. Statins therefore have a place in the secondary prevention of cardiovascular disease. Recent experimental and clinical studies suggest that statins may exert vascular protective effect beyond cholesterol reduction. The cholesterol-independet or “pleiotropic” effects of statin include the upregulation and activation of endothelial nitric acid synthase (eNOS that can increase nitric oxide (NO production. Augmentation of NO production increases cerebral blood flow, which can lead to neuroprotection during brain ischaemia. By inhibiting mevalonate synthesis, statins prevent the formation of several isoprenoids (including farnesylpyrophosphate and geranylgeranylpyrophosphate. Inhibiting geranylgeranylation of RhoA small G proteins increases the stability of eNOS mRNA through the remodeling of endothelial actin microfilamens. Moreover, statins directly increase eNOS activity within minutes by activating the pathway involving phosphoinositide 3-kinase and protein kinase B. In the secondary prevention of stroke, the use of statins reduces the incidence of either recurrent stroke or other major vascular events and treatment should be initiated soon after the event. The use of statins does not increase hemorrhagic stroke or cancer and may also favor atherosclerotic plaque regression.

  3. Pleiotropic effects of statins in stroke prevention

    Directory of Open Access Journals (Sweden)

    Yenny Yenny

    2016-02-01

    Full Text Available Cardiovascular disease is the leading cause of death and disability, and  contributes substantially to healthcare budgets. The lipid-lowering drugs, 3-hydroxy-3-methylgulutaryl-coenzyme A (HMG-CoA reductase inhibitor or statins, reducing mortality and cardiovascular morbidity in patients with established cardiovascular disease. Statins therefore have a place in the secondary prevention of cardiovascular disease. Recent experimental and clinical studies suggest that statins may exert vascular protective effect beyond cholesterol reduction. The cholesterol-independet or “pleiotropic” effects of statin include the upregulation and activation of endothelial nitric acid synthase (eNOS that can increase nitric oxide (NO production. Augmentation of NO production increases cerebral blood flow, which can lead to neuroprotection during brain ischaemia. By inhibiting mevalonate synthesis, statins prevent the formation of several isoprenoids (including farnesylpyrophosphate and geranylgeranylpyrophosphate. Inhibiting geranylgeranylation of RhoA small G proteins increases the stability of eNOS mRNA through the remodeling of endothelial actin microfilamens. Moreover, statins directly increase eNOS activity within minutes by activating the pathway involving phosphoinositide 3-kinase and protein kinase B. In the secondary prevention of stroke, the use of statins reduces the incidence of either recurrent stroke or other major vascular events and treatment should be initiated soon after the event. The use of statins does not increase hemorrhagic stroke or cancer and may also favor atherosclerotic plaque regression.

  4. Effects of Adherence to Statin Therapy on Health Care Outcomes and Utilizations in Taiwan: A Population-Based Study

    Directory of Open Access Journals (Sweden)

    Ying-Chun Li

    2015-01-01

    Full Text Available Aim. Good medication adherence may decrease the probability of worse outcomes and reduce unnecessary medical care costs. This study aims to evaluate medication adherence for people on statin therapy. Methods. National health insurance databases were analyzed from January 1, 2001, to December 31, 2007. Study samples were patients of 45 years and older adults who took statin for the first time during the study period. Medication possession ratio (MPR was measured until the patients had hospitalization or reached the three-year follow-up period. We identified a good (MPR ≥ 80% and a poor (MPR < 80% medication adherence group to conduct statistical analyses. Results. 40.8% of patients were of good medication adherence and 59.2% were of poor medication adherence. Multivariate logistic regression model indicated that the MPR ≥ 80% group had significantly less probability of hospitalization (P<0.001. Being men, increasing age, higher Charlson Comorbidity Index (CCI scores, seeking care mostly in the medical center or teaching hospitals, and living in the suburban or rural areas had higher probability of hospitalization (P<0.05 or P<0.001. The MPR ≥ 80% group spent less hospitalization expenditures (P<0.001. Conclusion. Effective interventions may be applied to the poor medication adherence group in order to improve their health care outcomes.

  5. Gleaning data from disaster: a hospital-based data mining method to study all-hazard triage after a chemical disaster.

    Science.gov (United States)

    Craig, Jean B; Culley, Joan M; Tavakoli, Abbas S; Svendsen, Erik R

    2013-01-01

    To describe the methods of evaluating currently available triage models for their efficacy in appropriately triaging the surge of patients after an all-hazards disaster. A method was developed for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster. On January 6, 2005, a freight train carrying three tanker cars of liquid chlorine was inadvertently switched onto an industrial spur in central Graniteville, SC. The train then crashed into a parked locomotive and derailed. This caused one of the chlorine tankers to rupture and immediately release ~60 tons of chlorine. Chlorine gas infiltrated the town with a population of 7,000. This research focuses on the victims who received emergency care in South Carolina. With our data mapping and decision tree logic, the authors were successful in using the available extracted clinical data to estimate triage categories for use in our study. The methodology outlined in this article shows the potential use of well-designed secondary analysis methods to improve mass casualty research. The steps are reliable and repeatable and can easily be extended or applied to other disaster datasets.

  6. Data Mining the Corporate Dental System of USA DENTAC Fort Bragg

    Science.gov (United States)

    2016-06-10

    Data Mining the Corporate Dental System of USA DENTAC Fort Bragg FREDWIN HOLOMON, D.D.S. B.S. University of...thesis manuscript entitled: Data Mining the Corporate Dental System of USA DENTAC Fort Bragg Is appropriately acknowledged and beyond visual...The present study collected data from the Corporate Dental System encompassing the time period between October 2014 and October 2015. Patient

  7. Statin treatment in multiple sclerosis

    DEFF Research Database (Denmark)

    Pihl-Jensen, Gorm; Tsakiri, Anna; Frederiksen, Jette Lautrup

    2015-01-01

    BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory disease that leads to progressive disability. Statins [hydroxymethylglutaryl-CoA (HMG-CoA) reductase inhibitors] are widely prescribed drugs in hypercholesterolemia. They exert immunomodulatory and neurotrophic effects and are attractive...... of relapse activity, magnetic resonance imaging (MRI) activity, Expanded Disability Status Scale (EDSS) progression, and adverse events using a fixed-effects model due to low heterogeneity between studies. RESULTS: Eight trials were included in the review [five of statin add-on to interferon (IFN......, proportion of patients with relapse, and whole brain atrophy but not for EDSS progression. In SPMS, statin monotherapy showed significant reduction in brain atrophy and disability progression but no effect on relapse rate. In CIS, a phase II trial showed no difference in relapse activity, MRI activity...

  8. Increasing incidence of statin prescribing for the elderly without previous cardiovascular conditions:  A nation wide register study

    DEFF Research Database (Denmark)

    Kildemoes, Helle Wallach; Andersen, Morten

    Supported by the growing evidence of the beneficial effects of statins in a range of conditions, statin utilization has increased considerably in most Western countries over the last decade. Objectives To estimate to what extent a widening of indication scope for statins accounts for the increasi...... for initiating statin treatment, including the "abolition of ageism". The fact that treatment incidence grew most among elderly without disease markers reflects a changing prescribing behaviours among general practitioners, presumably related to an increased use of risk scoring....

  9. Adherence to drug label recommendations for avoiding drug interactions causing statin-induced myopathy--a nationwide register study.

    Directory of Open Access Journals (Sweden)

    Jennifer Settergren

    Full Text Available PURPOSE: To investigate the extent to which clinicians avoid well-established drug-drug interactions that cause statin-induced myopathy. We hypothesised that clinicians would avoid combining erythromycin or verapamil/diltiazem respectively with atorvastatin or simvastatin. In patients with statin-fibrate combination therapy, we hypothesised that gemfibrozil was avoided to the preference of bezafibrate or fenofibrate. When combined with verapamil/diltiazem or fibrates, we hypothesized that the dispensed doses of atorvastatin/simvastatin would be decreased. METHODS: Cross-sectional analysis of nationwide dispensing data. Odds ratios of interacting erythromycin, verapamil/diltiazem versus respective prevalence of comparator drugs doxycycline, amlodipine/felodipine in patients co-dispensed interacting statins simvastatin/atorvastatin versus patients unexposed (pravastatin/fluvastatin/rosuvastatin was calculated. For fibrates, OR of gemfibrozil versus fenofibrate/bezafibrate in patients co-dispensed any statin was assessed. RESULTS: OR of interacting erythromycin versus comparator doxycycline did not differ between patients on interacting and comparator statins either in patients dispensed high or low statin doses (adjusted OR 0.87; 95% CI 0.60-1.25 and 0.92; 95% CI 0.69-1.23. Interacting statins were less common among patients dispensed verapamil/diltiazem as compared to patients on amlodipine/felodipine (OR high dose 0.62; CI 0.56-0.68 and low dose 0.63; CI 0.58-0.68. Patients on any statin were to a lesser extent dispensed gemfibrozil compared to patients not dispensed a statin (OR high dose 0.65; CI 0.55-0.76 and low dose 0.70; CI 0.63-0.78. Mean DDD (SD for any statin was substantially higher in patients co-dispensed gemfibrozil 178 (149 compared to patients on statin monotherapy 127 (93, (p<0.001. CONCLUSIONS: Prescribers may to some extent avoid co-prescription of statins with calcium blockers and fibrates with an increased risk of myopathy

  10. Detecting Internet Worms Using Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Muazzam Siddiqui

    2008-12-01

    Full Text Available Internet worms pose a serious threat to computer security. Traditional approaches using signatures to detect worms pose little danger to the zero day attacks. The focus of malware research is shifting from using signature patterns to identifying the malicious behavior displayed by the malwares. This paper presents a novel idea of extracting variable length instruction sequences that can identify worms from clean programs using data mining techniques. The analysis is facilitated by the program control flow information contained in the instruction sequences. Based upon general statistics gathered from these instruction sequences we formulated the problem as a binary classification problem and built tree based classifiers including decision tree, bagging and random forest. Our approach showed 95.6% detection rate on novel worms whose data was not used in the model building process.

  11. Parallel object-oriented data mining system

    Science.gov (United States)

    Kamath, Chandrika; Cantu-Paz, Erick

    2004-01-06

    A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

  12. Statin use and risk of endometrial cancer

    DEFF Research Database (Denmark)

    Sperling, Cecilie D.; Verdoodt, Freija; Friis, Soren

    2017-01-01

    INTRODUCTION: Laboratory and epidemiological evidence have suggested that statin use may protect against the development of certain cancers, including endometrial cancer. In a nationwide registry-based case-control study, we examined the association between statin use and risk of endometrial cancer....... MATERIAL AND METHODS: Cases were female residents of Denmark with a primary diagnosis of endometrial cancer during 2000-2009. For each case, we selected 15 female population controls matched on date of birth (±one month) using risk-set sampling. Ever use of statin was defined as two or more prescriptions...... 5382 endometrial cancer cases and 72 127 population controls. We observed no association between ever use of statins and endometrial cancer risk (OR 1.03, 95% CI 0.94-1.14). In addition, endometrial cancer risk did not vary substantially with duration or intensity of statin use. Stratification by type...

  13. Visualizing data mining results with the Brede tools

    DEFF Research Database (Denmark)

    Nielsen, Finn Årup

    2009-01-01

    has expanded and now includes its own database with coordinates along with ontologies for brain regions and functions: The Brede Database. With Brede Toolbox and Database combined we setup automated workflows for extraction of data, mass meta-analytic data mining and visualizations. Most of the Web......A few neuroinformatics databases now exist that record results from neuroimaging studies in the form of brain coordinates in stereotaxic space. The Brede Toolbox was originally developed to extract, analyze and visualize data from one of them --- the BrainMap database. Since then the Brede Toolbox...

  14. Validity of association rules extracted by healthcare-data-mining.

    Science.gov (United States)

    Takeuchi, Hiroshi; Kodama, Naoki

    2014-01-01

    A personal healthcare system used with cloud computing has been developed. It enables a daily time-series of personal health and lifestyle data to be stored in the cloud through mobile devices. The cloud automatically extracts personally useful information, such as rules and patterns concerning the user's lifestyle and health condition embedded in their personal big data, by using healthcare-data-mining. This study has verified that the extracted rules on the basis of a daily time-series data stored during a half- year by volunteer users of this system are valid.

  15. Applying Fuzzy Data Mining to Telecom Churn Management

    Science.gov (United States)

    Liao, Kuo-Hsiung; Chueh, Hao-En

    Customers tend to change telecommunications service providers in pursuit of more favorable telecommunication rates. Therefore, how to avoid customer churn is an extremely critical topic for the intensely competitive telecommunications industry. To assist telecommunications service providers in effectively reducing the rate of customer churn, this study used fuzzy data mining to determine effective marketing strategies by analyzing the responses of customers to various marketing activities. These techniques can help telecommunications service providers determine the most appropriate marketing opportunities and methods for different customer groups, to reduce effectively the rate of customer turnover.

  16. Machine Learning and Data Mining Methods in Diabetes Research

    Directory of Open Access Journals (Sweden)

    Ioannis Kavakiotis

    Full Text Available The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs. To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc. has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a Prediction and Diagnosis, b Diabetic Complications, c Genetic Background and Environment, and e Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM. Keywords: Machine learning, Data mining, Diabetes mellitus, Diabetic complications, Disease prediction models, Biomarker(s identification

  17. Long-Term Safety and Efficacy of Lowering Low-Density Lipoprotein Cholesterol With Statin Therapy: 20-Year Follow-Up of West of Scotland Coronary Prevention Study.

    Science.gov (United States)

    Ford, Ian; Murray, Heather; McCowan, Colin; Packard, Chris J

    2016-03-15

    Extended follow-up of statin-based low-density lipoprotein cholesterol lowering trials improves the understanding of statin safety and efficacy. Examining cumulative cardiovascular events (total burden of disease) gives a better appreciation of the clinical value of statins. This article evaluates the long-term impact of therapy on mortality and cumulative morbidity in a high-risk cohort of men. The West of Scotland Coronary Prevention Study was a primary prevention trial in 45- to 64-year-old men with high low-density lipoprotein cholesterol. A total of 6595 men were randomized to receive pravastatin 40 mg once daily or placebo for an average of 4.9 years. Subsequent linkage to electronic health records permitted analysis of major incident events over 20 years. Post trial statin use was recorded for 5 years after the trial but not for the last 10 years. Men allocated to pravastatin had reduced all-cause mortality (hazard ratio, 0.87; 95% confidence interval, 0.80-0.94; P=0.0007), attributable mainly to a 21% decrease in cardiovascular death (hazard ratio, 0.79; 95% confidence interval, 0.69-0.90; P=0.0004). There was no difference in noncardiovascular or cancer death rates between groups. Cumulative hospitalization event rates were lower in the statin-treated arm: by 18% for any coronary event (P=0.002), by 24% for myocardial infarction (P=0.01), and by 35% for heart failure (P=0.002). There were no significant differences between groups in hospitalization for noncardiovascular causes. Statin treatment for 5 years was associated with a legacy benefit, with improved survival and a substantial reduction in cardiovascular disease outcomes over a 20-year period, supporting the wider adoption of primary prevention strategies. © 2016 The Authors.

  18. Data mining algorithm for discovering matrix association regions (MARs)

    Science.gov (United States)

    Singh, Gautam B.; Krawetz, Shephan A.

    2000-04-01

    Lately, there has been considerable interest in applying Data Mining techniques to scientific and data analysis problems in bioinformatics. Data mining research is being fueled by novel application areas that are helping the development of newer applied algorithms in the field of bioinformatics, an emerging discipline representing the integration of biological and information sciences. This is a shift in paradigm from the earlier and the continuing data mining efforts in marketing research and support for business intelligence. The problem described in this paper is along a new dimension in DNA sequence analysis research and supplements the previously studied stochastic models for evolution and variability. The discovery of novel patterns from genetic databases as described is quite significant because biological patterns play an important role in a large variety of cellular processes and constitute the basis for gene therapy. Biological databases containing the genetic codes from a wide variety of organisms, including humans, have continued their exponential growth over the last decade. At the time of this writing, the GenBank database contains over 300 million sequences and over 2.5 billion characters of sequenced nucleotides. The focus of this paper is on developing a general data mining algorithm for discovering regions of locus control, i.e. those regions that are instrumental for determining cell type. One such type of element of locus control are the MARs or the Matrix Association Regions. Our limited knowledge about MARs has hampered their detection using classical pattern recognition techniques. Consequently, their detection is formulated by utilizing a statistical interestingness measure derived from a set of empirical features that are known to be associated with MARs. This paper presents a systematic approach for finding associations between such empirical features in genomic sequences, and for utilizing this knowledge to detect biologically interesting

  19. Using data mining to segment healthcare markets from patients' preference perspectives.

    Science.gov (United States)

    Liu, Sandra S; Chen, Jie

    2009-01-01

    This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics. Data were derived from a number of individuals who received in-patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables. Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles. The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters. Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.

  20. From Social Data Mining to Forecasting Socio-Economic Crisis

    OpenAIRE

    Helbing, Dirk; Balietti, Stefano

    2010-01-01

    Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagin...

  1. Increasing incidence of statin treatmentamong the elderly and those withoutprevious cardiovascular conditions. A nationwide register study

    DEFF Research Database (Denmark)

    Kildemoes, Helle Wallach; Andersen, Morten

    +), with an overall IRR of 6.8 (6.4 to 7.1) and among those with no disease markers IRR 10.4 (9.5 to 11.3). Conclusions: Growing statin utilization reflects a widening of statin indication and changing prescribing behaviors, including the abolition of ageism. Treatment incidence grew most among the elderly without...

  2. Appropriate medical data categorization for data mining classification techniques.

    Science.gov (United States)

    Liao, Shang-Chih; Lee, I-Nong

    2002-03-01

    Some data mining (DM) methods, or software tools, require normalized data, others rely on categorized data, and some can accommodate multiple data scales. Each DM technique has a specific background theory; therefore, different results are expected when applying multiple methods. The purpose of this study is to find the data format appropriate for each DM classification technique for wider applications, and efficiently to obtain trustworthy results. Considering the nature of medical data, categorical variables are sometimes useful for making decisions and can make it easier to extrapolate knowledge. In this study, three mathematical data categorization methods (Fusinter, minimum description length principle [MDLPC] and Chi-merge) were applied to accommodate five data mining classification techniques (statistics discriminant analysis, supervised classification with Neural Networks, Decision trees, Genetic supervised clustering and Bayesian classification [probability neural networks; PNN]) using a heart disease database with four types of data (continuous data, binary data, nominal data, and ordinal data). Compared with original or normalized data, data categorized by the MDLPC categorization method was found to perform better in most of the DM classification techniques used in this study. Categorical data is good for most DM classification techniques (e.g. classification of disease and non-disease groups) and is relatively easy to use for extracting medical knowledge.

  3. Statin and NSAID Use and Prostate Cancer Risk

    Science.gov (United States)

    Coogan, Patricia F.; Kelly, Judith Parsells; Strom, Brian L.; Rosenberg, Lynn

    2010-01-01

    Purpose Some studies have reported reduced risks of advanced, but not early, prostate cancer among statin users, and one study found a reduced risk only among statin users who had also used nonsteroidal anti-inflammatory drugs (NSAIDs). We have previously reported no association between statin use and prostate cancer in our hospital-based Case Control Surveillance Study. The purpose of the present analyses was to update the findings by cancer stage and to evaluate the joint use of statins and NSAIDs. Methods Cases were 1367 men with prostate cancer and controls were 2007 men with diagnoses unrelated to statin or NSAID use. We used multivariable logistic regression analyses to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for statin use compared with no use, and joint use of statin and NSAIDs compared with use of neither. Results The odds ratio among regular statin users was 1.1 (95% CI 0.9–1.5), and odds ratios were similar among early and late stage cancers. The odds ratio among joint statin and NSAID users was 1.1 (95% CI 0.7–1.6). Conclusion The present results do not support a protective effect of statin use, or statin and NSAID use, on the risk of advanced prostate cancer. PMID:20582910

  4. Data Mining Practical Machine Learning Tools and Techniques

    CERN Document Server

    Witten, Ian H; Hall, Mark A

    2011-01-01

    Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

  5. Comparison of change in coronary atherosclerosis in patients with stable versus unstable angina pectoris receiving statin therapy (from the Treatment With Statin on Atheroma Regression Evaluated by Intravascular Ultrasound With Virtual Histology [TRUTH] study).

    Science.gov (United States)

    Nozue, Tsuyoshi; Yamamoto, Shingo; Tohyama, Shinichi; Fukui, Kazuki; Umezawa, Shigeo; Onishi, Yuko; Kunishima, Tomoyuki; Sato, Akira; Nozato, Toshihiro; Miyake, Shogo; Takeyama, Youichi; Morino, Yoshihiro; Yamauchi, Takao; Muramatsu, Toshiya; Hibi, Kiyoshi; Terashima, Mitsuyasu; Michishita, Ichiro

    2013-04-01

    Although statin-induced regression in coronary atherosclerosis seems to be greater in patients with acute coronary syndrome than in those with stable coronary artery disease, no reports have examined this. The purpose of the present study was to compare the changes in coronary atherosclerosis in patients with stable versus unstable angina pectoris (AP). The effects of 8-month statin therapy on coronary atherosclerosis were evaluated using virtual histology intravascular ultrasound, and analyzable intravascular ultrasound data were obtained from 119 patients (83 patients with stable AP and 36 with unstable AP). A significant decrease in plaque volume was observed in patients with unstable AP (-2.2%, p = 0.02) but not in patients with stable AP. A significant increase in the necrotic-core component (0.30 mm(3)/mm, p = 0.009) was observed only in patients with unstable AP. Significant positive correlations were observed between the percentage of change in platelet-activating factor acetylhydrolase and the percentage of change in plaque volume (r = 0.346, p = 0.05) in patients with unstable AP. No significant correlations were observed in patients with stable AP. Multivariate regression analyses showed that a reduction in platelet-activating factor acetylhydrolase was associated with regression in coronary atherosclerosis, particularly of the fibrous component (β = 0.443, p = 0.003), in patients with unstable AP. In conclusion, regression of the coronary artery plaque volume was greater, although statin therapy did not halt the increases in plaque vulnerability, in patients with unstable AP compared to those with stable AP. A reduction in the serum platelet-activating factor acetylhydrolase level was associated with regression in coronary atherosclerosis, particularly the fibrous plaque volume, in patients with unstable AP. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. Efficacy and safety of alirocumab in patients with hypercholesterolemia not adequately controlled with non-statin lipid-lowering therapy or the lowest strength of statin: ODYSSEY NIPPON study design and rationale.

    Science.gov (United States)

    Teramoto, Tamio; Kondo, Akira; Kiyosue, Arihiro; Harada-Shiba, Mariko; Ishigaki, Yasushi; Tobita, Kimimasa; Kawabata, Yumiko; Ozaki, Asuka; Baccara-Dinet, Marie T; Sata, Masataka

    2017-06-17

    Statins are generally well-tolerated and serious side effects are infrequent, but some patients experience adverse events and reduce their statin dose or discontinue treatment altogether. Alirocumab is a highly specific, fully human monoclonal antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), which can produce substantial and sustained reductions of low-density lipoprotein cholesterol (LDL-C). The randomized, double-blind, placebo-controlled, parallel-group, phase 3 ODYSSEY NIPPON study will explore alirocumab 150 mg every 4 weeks (Q4W) in 163 Japanese patients with hypercholesterolemia who are on the lowest-strength dose of atorvastatin (5 mg/day) or are receiving a non-statin lipid-lowering therapy (LLT) (fenofibrate, bezafibrate, ezetimibe, or diet therapy alone). Hypercholesterolemia is defined as LDL-C ≥ 100 mg/dL (2.6 mmol/L) in patients with heterozygous familial hypercholesterolemia or non-familial hypercholesterolemia with a history of documented coronary heart disease, or ≥120 mg/dL (3.1 mmol/L) in patients with non-familial hypercholesterolemia classified as primary prevention category III (i.e. high-risk patients). During the 12-week double-blind treatment period, patients will be randomized (1:1:1) to receive alirocumab subcutaneously (SC) 150 mg Q4W alternating with placebo for alirocumab Q4W, or alirocumab 150 mg SC every 2 weeks (Q2W), or SC placebo Q2W. The primary efficacy endpoint is the percentage change in calculated LDL-C from baseline to week 12. The long-term safety and tolerability of alirocumab will also be investigated. The ODYSSEY NIPPON study will provide insights into the efficacy and safety of alirocumab 150 mg Q4W or 150 mg Q2W among Japanese patients with hypercholesterolemia who are on the lowest-strength dose of atorvastatin, or are receiving a non-statin LLT (including diet therapy alone). ClinicalTrials.gov number: NCT02584504.

  7. The DYSlipidemia International Study (DYSIS-Egypt: A report on the prevalence of lipid abnormalities in Egyptian patients on chronic statin treatment

    Directory of Open Access Journals (Sweden)

    Adel El Etriby

    2013-09-01

    Conclusions: Despite chronic statin treatment, two-thirds of patients in the DYSIS-Egypt study had elevated LDL–C levels. A dual strategy, comprising modification of lifestyle factors together with novel treatment options, appears to be necessary to combat the rise in cardiovascular-related morbidity and mortality.

  8. Determinants of residual risk in secondary prevention patients treated with high- versus low-dose statin therapy: the Treating to New Targets (TNT) study

    NARCIS (Netherlands)

    Mora, Samia; Wenger, Nanette K.; Demicco, David A.; Breazna, Andrei; Boekholdt, S. Matthijs; Arsenault, Benoit J.; Deedwania, Prakash; Kastelein, John J. P.; Waters, David D.

    2012-01-01

    Cardiovascular events occur among statin-treated patients, albeit at lower rates. Risk factors for this "residual risk" have not been studied comprehensively. We aimed to identify determinants of this risk above and beyond lipid-related risk factors. A total of 9251 coronary patients with

  9. Relative effects of statin therapy on stroke and cardiovascular events in men and women: secondary analysis of the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) Study

    DEFF Research Database (Denmark)

    Goldstein, L.B.; Amarenco, P.; Lamonte, M.

    2008-01-01

    similarly benefited from randomization to statin treatment. METHODS: The effect of sex on treatment-related reductions in stroke and other cardiovascular outcomes were analyzed with Cox regression modeling testing for sex by treatment interactions. RESULTS: Women (n=1908) constituted 40% of the SPARCL study...

  10. Statin use in adults at high risk of cardiovascular disease mortality: cross-sectional analysis of baseline data from The Irish Longitudinal Study on Ageing (TILDA).

    LENUS (Irish Health Repository)

    Murphy, Catriona

    2015-07-01

    This study aims to examine the extent to which statins are used by adults at high risk of cardiovascular disease (CVD) compared to European clinical guidelines. The high-risk groups examined are those with (1) known CVD, (2) known diabetes and (3) a high or very high risk (≥5%) of CVD mortality based on Systematic COronary Risk Evaluation (SCORE).

  11. Application of Data Mining in direct marketing

    Directory of Open Access Journals (Sweden)

    Dejana Pavlović

    2014-04-01

    Full Text Available The key to successful business operations lies in good communication with clients. There are a growing number of brokers in the financial market who collect excess funds from the clients and perform transfers to those who need the funds. However, many external and internal factors influence the decision on disposal of available funds. This paper identifies and researches into clients’ satisfaction in the banking system. By application of the disclosure of data legality we will try to point to the factors that influence the clients' decision to invest their long-term deposits in the parent bank. Upon classification and clustering, we will interpret and indentify the strengths and weaknesses of the target results. This analysis provides the guidelines through the use of the decision-making tree, application of data mining and the possibility to use a large set of data increases the value and accuracy of this technique. The problem with this technique is accuracy of the data submitted by the client.

  12. Using Data Mining for Wine Quality Assessment

    Science.gov (United States)

    Cortez, Paulo; Teixeira, Juliana; Cerdeira, António; Almeida, Fernando; Matos, Telmo; Reis, José

    Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.

  13. DATA MINING APPLICATION IN CREDIT CARD FRAUD DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    FRANCISCA NONYELUM OGWUELEKA

    2011-06-01

    Full Text Available Data mining is popularly used to combat frauds because of its effectiveness. It is a well-defined procedure that takes data as input and produces models or patterns as output. Neural network, a data mining technique was used in this study. The design of the neural network (NN architecture for the credit card detection system was based on unsupervised method, which was applied to the transactions data to generate four clusters of low, high, risky and high-risk clusters. The self-organizing map neural network (SOMNN technique was used for solving the problem of carrying out optimal classification of each transaction into its associated group, since a prior output is unknown. The receiver-operating curve (ROC for credit card fraud (CCF detection watch detected over 95% of fraud cases without causing false alarms unlike other statistical models and the two-stage clusters. This shows that the performance of CCF detection watch is in agreement with other detection software, but performs better.

  14. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  15. Statin use and survival in colorectal cancer: Results from a population-based cohort study and an updated systematic review and meta-analysis.

    Science.gov (United States)

    Gray, Ronan T; Coleman, Helen G; Hughes, Carmel; Murray, Liam J; Cardwell, Chris R

    2016-12-01

    The aim of this study was to investigate the association between statin use and survival in a population-based colorectal cancer (CRC) cohort and perform an updated meta-analysis to quantify the magnitude of any association. A cohort of 8391 patients with newly diagnosed Dukes' A-C CRC (2009-2012) was identified from the Scottish Cancer Registry. This cohort was linked to the Prescribing Information System and the National Records of Scotland Death Records (until January 2015) to identify 1064 colorectal cancer-specific deaths. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer-specific mortality by statin use were calculated using time dependent Cox regression models. The systematic review included relevant studies published before January 2016. Meta-analysis techniques were used to derive combined HRs for associations between statin use and cancer-specific and overall mortality. In the Scottish cohort, statin use before diagnosis (HR=0.84, 95% CI 0.75-0.94), but not after (HR=0.90, 95% CI 0.77-1.05), was associated with significantly improved cancer-specific mortality. The systematic review identified 15 relevant studies. In the meta-analysis, there was consistent (I 2 =0%,heterogeneity P=0.57) evidence of a reduction in cancer-specific mortality with statin use before diagnosis in 6 studies (n=86,622, pooled HR=0.82, 95% CI 0.79-0.86) but this association was less apparent and more heterogeneous (I 2 =67%,heterogeneity P=0.03) with statin use after diagnosis in 4 studies (n=19,152, pooled HR=0.84, 95% CI 0.68-1.04). In a Scottish CRC cohort and updated meta-analysis there was some evidence that statin use was associated with improved survival. However, these associations were weak in magnitude and, particularly for post-diagnosis use, varied markedly between studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Equity in statin use in New Zealand

    Directory of Open Access Journals (Sweden)

    Norris P

    2014-03-01

    Full Text Available INTRODUCTION: Preventive medications such as statins are used to reduce cardiovascular risk. There is some evidence to suggest that people of lower socioeconomic position are less likely to be prescribed statins. In New Zealand, Maori have higher rates of cardiovascular disease. AIM: This study aimed to investigate statin utilisation by socioeconomic position and ethnicity in a region of New Zealand. METHODS: This was a cross-sectional study in which data were collected on all prescriptions dispensed from all pharmacies in one city during 2005/6. Linkage with national datasets provided information on patients' age, gender and ethnicity. Socioeconomic position was identified using the New Zealand Index of Socioeconomic Deprivation 2006. RESULTS: Statin use increased with age until around 75 years. Below age 65 years, those in the most deprived socioeconomic areas were most likely to receive statins. In the 55-64 age group, 22.3% of the most deprived population received a statin prescription (compared with 17.5% of the mid and 18.6% of the least deprived group. At ages up to 75 years, use was higher amongst Maori than non-Maori, particularly in middle age, where Maori have a higher risk of cardiovascular disease. In the 45-54 age group, 11.6% of Maori received a statin prescription, compared with 8.7% of non-Maori. DISCUSSION: Statin use approximately matched the pattern of need, in contrast to other studies which found under-treatment of people of low socioeconomic position. A PHARMAC campaign to increase statin use may have increased use in high-risk groups in New Zealand.

  17. Statins and risk of breast cancer recurrence

    Directory of Open Access Journals (Sweden)

    Sakellakis M

    2016-11-01

    Full Text Available Minas Sakellakis,1 Karolina Akinosoglou,1 Anastasia Kostaki,2 Despina Spyropoulou,1 Angelos Koutras,1 1Department of Medicine, Division of Oncology, University Hospital, Patras Medical School, Patras, 2Department of Statistics, Athens University of Economics and Business, Athens, Greece Background: The primary end point of our study was to test whether the concurrent use of a statin is related to a lower risk of recurrence and increased relapse-free survival in patients with early breast cancer. Materials and methods: We reviewed 610 female patients with stage I, II, or III breast cancer who had been surgically treated and who had subsequently received at least adjuvant chemotherapy in order to prevent recurrence. Results: Among the 610 patients with breast cancer, 83 (13.6% were receiving a statin on a chronic basis for other medical purposes. Overall, statin users displayed longer mean relapse-free survival (16.6 vs 10.2 years, P=0.028. After data had been adjusted for patient and disease characteristics, statin users maintained a lower risk of recurrence. This favorable outcome in statin users was particularly evident when we included only younger patients in the analysis (20 vs 10 years, P=0.006. Conclusion: Statins may be linked to a favorable outcome in early breast cancer patients, especially in younger age-groups. Keywords: statins, breast, cancer, adjuvant, recurrence

  18. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study.

    Science.gov (United States)

    Shouval, Roni; Labopin, Myriam; Bondi, Ori; Mishan-Shamay, Hila; Shimoni, Avichai; Ciceri, Fabio; Esteve, Jordi; Giebel, Sebastian; Gorin, Norbert C; Schmid, Christoph; Polge, Emmanuelle; Aljurf, Mahmoud; Kroger, Nicolaus; Craddock, Charles; Bacigalupo, Andrea; Cornelissen, Jan J; Baron, Frederic; Unger, Ron; Nagler, Arnon; Mohty, Mohamad

    2015-10-01

    Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation-related mortality risk prediction. This work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data. OM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; Prisk evaluation of patients with AL before HSCT, and is available online (http://bioinfo.lnx.biu.ac.il/∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT. © 2015 by American Society of Clinical Oncology.

  19. Statins: Mechanisms of neuroprotection

    NARCIS (Netherlands)

    van der Most, Peter J.; Dolga, Amalia; Nijholt, Ingrid M.; Luiten, Paul G. M.; Eisel, Ulrich L. M.

    Clinical trials report that the class of drugs known as statins may be neuroprotective in Alzheimer's and Parkinson's disease, and further trials are currently underway to test whether these drugs are also beneficial in multiple sclerosis and acute stroke treatment. Since statins are well tolerated

  20. Statin resistance and export

    DEFF Research Database (Denmark)

    2015-01-01

    The present invention relates e.g. to methods of producing statins in transgenic, non-filamentous microorganisms such as Saccharomyces cerevisiae. In addition, the present invention relates to the transgenic, non-filamentous microorganisms as such as well as various uses of transmembrane statin e...

  1. Achievement of cholesterol targets and prescribing of higher-cost statins: a cross-sectional study in general practice.

    Science.gov (United States)

    Fleetcroft, Robert; Schofield, Peter; Duerden, Martin; Ashworth, Mark

    2012-12-01

    There is conflicting evidence as to whether achievement of cholesterol targets at the population level is dependent on the choice and cost of statin. To investigate the practice-level relationship between cholesterol quality indicators in patients with heart disease, stroke, and diabetes and prescribing of low-cost statins. Correlations and linear regression modelling of retrospective cross-sectional practice-level data with potential explanatory variables in 7909 (96.4%) general practices in England in 2008-2009. Quality indicator data were obtained from the Information Centre and prescribing data from the NHS Business Authority. A 'cholesterol quality indicator' score was constructed by dividing the numbers of patients achieving the target for cholesterol control of ≤5 mmol/l in stroke, diabetes, and heart disease by the numbers on each register. A 'low-cost statin' ratio score was constructed by dividing the numbers of defined daily doses of simvastatin and pravastatin by the total numbers of defined daily doses of statins. Simvastatin accounted for 83.3% (standard deviation [SD] = 15.7%) of low-cost statins prescribed and atorvastatin accounted for 85.7% (SD = 14.8%) of high-cost statins prescribed. The mean cholesterol score was 73.7% (SD = 6.0%). Practices using a higher proportion of the low-cost statins were less successful in achieving cholesterol targets. An increase of 10% in the prescribing of low-cost statins was associated with a decrease of 0.46% in the cholesterol quality indicator score (95% confidence interval = -0.54% to -0.38%, Pcost statins was associated with a small reduction in cholesterol control.

  2. Non-response to (statin) therapy

    DEFF Research Database (Denmark)

    Trompet, S; Postmus, I; Slagboom, P E

    2016-01-01

    : Baseline characteristics of non-responders to statin therapy (≤10 % LDL-C reduction) were compared with those of high responders (>40 % LDL-C reduction) through a linear regression analysis. In addition, pharmacogenetic candidate gene analysis was performed to show the effect of excluding non......-responders from the analysis. RESULTS: Non-responders to statin therapy were younger (p = 0.001), more often smoked (p ....035) compared to subjects who highly responded to pravastatin treatment. Moreover, excluding non-responders from pharmacogenetic studies yielded more robust results, as standard errors decreased. CONCLUSION: Our results suggest that non-responders to statin therapy are more likely to actually be non...

  3. Application of data mining techniques in healthcare database ...

    African Journals Online (AJOL)

    Healthcare system have several and vast databases. Valuable information can be extracted from these data stores through the application of data mining techniques. Data mining is a powerful tool that will help healthcare system to focus on the data warehouse important information as it extracts hidden predictive ...

  4. Set-oriented data mining in relational databases

    NARCIS (Netherlands)

    Houtsma, M.A.W.; Swami, Arun

    1995-01-01

    Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are

  5. Exploring the Integration of Data Mining and Data Visualization

    Science.gov (United States)

    Zhang, Yi

    2011-01-01

    Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…

  6. The viability of business data mining in the sports environment ...

    African Journals Online (AJOL)

    Data mining can be viewed as the process of extracting previously unknown information from large databases and utilising this information to make crucial business decisions (Simoudis, 1996: 26). This paper considers the viability of using data mining tools and techniques in sports, particularly with regard to mining the ...

  7. Experienced ethical issues of personalized data-mined media services

    DEFF Research Database (Denmark)

    Sørensen, Jannick Kirk

    2008-01-01

    This tentative PhD project description concerns the ethnographic examination of users’ experience of privacy issues and usability related to personalized data mined (web-) services for media content.......This tentative PhD project description concerns the ethnographic examination of users’ experience of privacy issues and usability related to personalized data mined (web-) services for media content....

  8. Data Mine and Forget It?: A Cautionary Tale

    Science.gov (United States)

    Tada, Yuri; Kraft, Norbert Otto; Orasanu, Judith M.

    2011-01-01

    With the development of new technologies, data mining has become increasingly popular. However, caution should be exercised in choosing the variables to include in data mining. A series of regression trees was created to demonstrate the change in the selection by the program of significant predictors based on the nature of variables.

  9. Data Mining Technologies for Blood Glucose and Diabetes Management

    NARCIS (Netherlands)

    Bellazzi, Riccardo; Abu-Hanna, Ameen

    2009-01-01

    Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management

  10. (NHIS) using data mining technique as a statistical model

    African Journals Online (AJOL)

    kofi.mereku

    2014-05-23

    May 23, 2014 ... Scheme (NHIS) claims in the Awutu-Effutu-Senya District using data mining techniques, with a specific focus on .... transform them into a format that is friendly to data mining algorithms, such as .... many groups to access the data, facilitate updating the data, and improve the efficiency of checking the data for ...

  11. Application and Exploration of Big Data Mining in Clinical Medicine.

    Science.gov (United States)

    Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

    2016-03-20

    To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.

  12. Data mining in e-commerce: A survey

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    Abstract. Data mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Our intent is not to survey the ...

  13. Informatics, Data Mining, Econometrics and Financial Economics: A Connection

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)

    2015-01-01

    textabstractThis short communication reviews some of the literature in econometrics and financial economics that is related to informatics and data mining. We then discuss some of the research on econometrics and financial economics that could be extended to informatics and data mining beyond the

  14. Statin-associated muscle symptoms: impact on statin therapy

    DEFF Research Database (Denmark)

    Stroes, Erik S; Thompson, Paul D; Corsini, Alberto

    2015-01-01

    -associated myopathy, and provides guidance for diagnosis and management of SAMS. Statin-associated myopathy, with significant elevation of serum creatine kinase (CK), is a rare but serious side effect of statins, affecting 1 per 1000 to 1 per 10 000 people on standard statin doses. Statin-associated muscle symptoms...

  15. The impact of data mining techniques on medical diagnostics

    Directory of Open Access Journals (Sweden)

    Siri Krishan Wasan

    2006-11-01

    Full Text Available Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. However, the available raw medical data are widely distributed, heterogeneous in nature, and voluminous. These data need to be collected in an organized form. This collected data can be then integrated to form a hospital information system. Data mining technology provides a user-oriented approach to novel and hidden patterns in the data. Data mining and statistics both strive towards discovering patterns and structures in data. Statistics deals with heterogeneous numbers only, whereas data mining deals with heterogeneous fields. We identify a few areas of healthcare where these techniques can be applied to healthcare databases for knowledge discovery. In this paper we briefly examine the impact of data mining techniques, including artificial neural networks, on medical diagnostics.

  16. Software tool for data mining and its applications

    Science.gov (United States)

    Yang, Jie; Ye, Chenzhou; Chen, Nianyi

    2002-03-01

    A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

  17. A review of contrast pattern based data mining

    Science.gov (United States)

    Zhu, Shiwei; Ju, Meilong; Yu, Junfeng; Cai, Binlei; Wang, Aiping

    2015-07-01

    Contrast pattern based data mining is concerned with the mining of patterns and models that contrast two or more datasets. Contrast patterns can describe similarities or differences between the datasets. They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust clusters and classifiers. The increasing use of contrast pattern data mining has initiated a great deal of research and development attempts in the field of data mining. A comprehensive revision on the existing contrast pattern based data mining research is given in this paper. They are generally categorized into background and representation, definitions and mining algorithms, contrast pattern based classification, clustering, and other applications, the research trends in future. The primary of this paper is to server as a glossary for interested researchers to have an overall picture on the current contrast based data mining development and identify their potential research direction to future investigation.

  18. Kajian Data Mining Customer Relationship Management pada Lembaga Keuangan Mikro

    Directory of Open Access Journals (Sweden)

    Tikaridha Hardiani

    2016-01-01

    Full Text Available Companies are required to be ready to face the competition will be intense with other companies, including micro-finance institutions. Faced more intense competition, has led to many businesses in microfinance institutions find profitable strategy to distinguish from the others. Strategy that can be applied is implementing Customer Relationship Management (CRM and data mining. Data mining can be used to microfinance institutions that have a large enough data. Determine the potential customers with customer segmentation can help the decision-making marketing strategy that will be implemented . This paper discusses several data mining techniques that can be used for customer segmentation. Proposed method of data mining technique is fuzzy clustering with fuzzy C-Means algorithm and fuzzy RFM. Keywords : Customer relationship management; Data mining; Fuzzy clustering; Micro-finance institutions; Fuzzy C-Means; Fuzzy RFM

  19. Big data mining analysis method based on cloud computing

    Science.gov (United States)

    Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

    2017-08-01

    Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

  20. A Six Sigma Methodology Using Data Mining: A Case Study on Six Sigma Project for Heat Efficiency Improvement of a Hot Stove System in a Korean Steel Manufacturing Company

    Science.gov (United States)

    Jang, Gil-Sang; Jeon, Jong-Hag

    Recently, Six Sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a Six Sigma methodology based on data mining for effectively and efficiently processing massive data in driving Six Sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a Korean steel company for improvement of heat efficiency and reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

  1. The role of metformin and statins in the incidence of epithelial ovarian cancer in type 2 diabetes: a cohort and nested case-control study.

    Science.gov (United States)

    Urpilainen, E; Marttila, M; Hautakoski, A; Arffman, M; Sund, R; Ilanne-Parikka, P; Arima, R; Kangaskokko, J; Puistola, U; Läärä, E; Hinkula, M

    2018-02-07

    To obtain evidence of the effects of metformin and statins on the incidence of ovarian cancer in women with type 2 diabetes (T2D). A retrospective cohort study and nested case-control study. The data were obtained from a diabetes database (FinDM) combining information from several nationwide registers. A cohort of 137 643 women over 40 years old and diagnosed with T2D during 1996-2011 in Finland. In full cohort analysis Poisson regression was used to estimate the hazard ratios (HR) in relation to ever use of metformin, insulin other oral anti-diabetic medication or statins. In the nested case-control analysis 20 controls were matched to each case of ovarian cancer. Conditional logistic regression was used to estimate HRs in relation to medication use and cumulative use of different medications. The estimates were adjusted for age and duration of T2D. Incidence of ovarian cancer. In all, 303 women were diagnosed with ovarian cancer during the follow up. Compared with other forms of oral anti-diabetic medication, metformin (HR 1.02, 95% CI: 0.72-1.45) was not found to be associated with the incidence of ovarian cancer. Neither was there evidence for statins to affect the incidence (HR 0.99, 95% CI: 0.78-1.25). In nested case-control analysis the results were essentially similar. No evidence of an association between the use of metformin or statins and the incidence of ovarian cancer in women with T2D was found. No evidence found for metformin or statins reducing the incidence of ovarian cancer. © 2018 Royal College of Obstetricians and Gynaecologists.

  2. Pleiotropic and Lipid–lowering Effects of Statins in Hypertension

    Science.gov (United States)

    Kamberi, Lulzim Selim; Bedri Bakalli, Aurora; Muhamet Budima, Norma; Rashit Gorani, Daut; Karabulut, Ahmet Muzaffer; Talat Pallaska, Kelmend

    2012-01-01

    Background: Data on the lowering effects of statins in hypertensive patients have been mixed and highly controversial. Some studies shows reductions effects of statins in blood pressure, whereas others do not. The evidence in the literature on the effects of statins on blood pressure raises the possibility that statins may directly lower blood pressure in addition to reduce cholesterol levels–pleiotropic effects of statins. Aim of the study: The role of statins as additional treatment in patients with severe hypertension and advanced aortic atherosclerotic plaques. Methods. We enrolled 62 patients. Study has been approved by Committee of Ethics and patients signed a Term of Free Informed Consent. All patients were studied with transoesophageal echocardiography at baseline and 12 months after enrolment. Inclusion criteria were severe hypertension and presence of aortic atherosclerotic plaques. Patients have been divided into two groups; group A (treated with antihypertenives and statins) and group B (treated, just with antihypertensives). Results: Twenty patients, of totally 38, from group A (20/38 or 52.6%) had significantly plaque reduction. One patient of totally 24 (1/24 or 4.1% ) from group B had significantly atherosclerotic plaque reduction. Difference of plaques reduction between two groups was highly significant. Regarding blood pressure levels, statins users had significantly reduction on systolic and diastolic blood pressure compared to statins nonusers. Conclusion: Hypertensive patients with presence of AA plaques treated with antihypertensives and statins have more BP reduction compared will hypertensive patients treated with antihypertensives alone. PMID:23678313

  3. Statin Intolerance: the Clinician's Perspective.

    Science.gov (United States)

    Stulc, Tomáš; Ceška, Richard; Gotto, Antonio M

    2015-12-01

    Muscle problems and other adverse symptoms associated with statin use are frequent reasons for non-adherence and discontinuation of statin therapy, which results in inadequate control of hyperlipidemia and increased cardiovascular risk. However, most patients who experience adverse symptoms during statin use are able to tolerate at least some degree of statin therapy. Given the profound cardiovascular benefits derived from statins, an adequate practical approach to statin intolerance is, therefore, of great clinical importance. Statin intolerance can be defined as the occurrence of myalgia or other adverse symptoms that are attributed to statin therapy and that lead to its discontinuation. In reality, these symptoms are actually unrelated to statin use in many patients, especially in those with atypical presentations following long periods of treatment. Thus, the first step in approaching patients with adverse symptoms during the course of statin therapy is identification of those patients for whom true statin intolerance is unlikely, since most of these patients would probably be capable of tolerating adequate statin therapy. In patients with statin intolerance, an altered dosing regimen of very low doses of statins should be attempted and, if tolerated, should gradually be increased to achieve the highest tolerable doses. In addition, other lipid-lowering drugs may be needed, either in combination with statins, or alone, if statins are not tolerated at all. Stringent control of other risk factors can aid in reducing cardiovascular risk if attaining lipid treatment goals proves difficult.

  4. [Application of data mining on the relationship betweendeqiand effect].

    Science.gov (United States)

    Pan, Qiuyin; Ma, Liangxiao; Yang, Yang; Zhu, Jiang

    2017-06-12

    To analyze the relationship between deqi and effect by data mining technique with retrieving clinical literature about acupuncture deqi since the founding of the country. The modern computerization and data mining technologies were adopted to set up clinical literature about acupuncture deqi database. The relevant clinical literature was collected, screened, extracted, and statistical and correlation analyses were used so as to explore the relationship between deqi and effect. 82.1% (46/56) of the studies considered that deqi was related to the effect; 17.9% (10/56) of the studies considered that deqi was unrelated to the effect. The support of deqi related to the effect is 100% on dysmenorrhea and facial paralysis. 72% (18/25) of the the articles of pain syndrome considered that deqi was related to the effect; 28% (7/25)of the studies considered that deqi was unrelated to the effect. In the research of the relationship between the features of deqi and effect, 60.7% of the studies suggested that the sense of conduction was positively correlated with the effect. There were 21 studies on the relationship between the intensity of deqi and effect, involving a variety of diseases, which was related to the type of the disease. 58.3% (7/12) of the articles on dysmenorrhea and 63% (34/54) on pain syndrome supported conduction positively correlated to effect, showing the highest frequencies. 50% (3/6) of the papers on facial paralysis supported weak deqi sensation positively correlated to effect, which was the highest frequency. Most studies considered that deqi can improve clinical efficacy. The relationship between different features of deqi and effect is closely related to the disease. Further study may focus on high quality research on the relationship between deqi and the obvious effect achieved by acupuncture so as to summarize the law of deqi .

  5. Collecting a citizen's digital footprint for health data mining.

    Science.gov (United States)

    Gencoglu, Oguzhan; Simila, Heidi; Honko, Harri; Isomursu, Minna

    2015-01-01

    This paper describes a case study for collecting digital footprint data for the purpose of health data mining. The case study involved 20 subjects residing in Finland who were instructed to collect data from registries which they evaluated to be useful for understanding their health or health behaviour, current or past. 11 subjects were active, sending 100 data requests to 49 distinct organizations in total. Our results indicate that there are still practical challenges in collecting actionable digital footprint data. Our subjects received a total of 75 replies (reply rate of 75.0%) and 61 datasets (reception rate of 61%). Out of the received data, 44 datasets (72.1%) were delivered in paper format, 4 (6.6%) in portable document format and 13 (21.3%) in structured digital form. The time duration between the sending of the information requests and reception of a reply was 26.4 days on the average.

  6. An Intelligent Archive Testbed Incorporating Data Mining

    Science.gov (United States)

    Ramapriyan, H.; Isaac, D.; Yang, W.; Bonnlander, B.; Danks, D.

    2009-01-01

    interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

  7. Data Mining Technologies Inspired from Visual Principle

    Science.gov (United States)

    Xu, Zongben

    In this talk we review the recent work done by our group on data mining (DM) technologies deduced from simulating visual principle. Through viewing a DM problem as a cognition problems and treading a data set as an image with each light point located at a datum position, we developed a series of high efficient algorithms for clustering, classification and regression via mimicking visual principles. In pattern recognition, human eyes seem to possess a singular aptitude to group objects and find important structure in an efficient way. Thus, a DM algorithm simulating visual system may solve some basic problems in DM research. From this point of view, we proposed a new approach for data clustering by modeling the blurring effect of lateral retinal interconnections based on scale space theory. In this approach, as the data image blurs, smaller light blobs merge into large ones until the whole image becomes one light blob at a low enough level of resolution. By identifying each blob with a cluster, the blurring process then generates a family of clustering along the hierarchy. The proposed approach provides unique solutions to many long standing problems, such as the cluster validity and the sensitivity to initialization problems, in clustering. We extended such an approach to classification and regression problems, through combatively employing the Weber's law in physiology and the cell response classification facts. The resultant classification and regression algorithms are proven to be very efficient and solve the problems of model selection and applicability to huge size of data set in DM technologies. We finally applied the similar idea to the difficult parameter setting problem in support vector machine (SVM). Viewing the parameter setting problem as a recognition problem of choosing a visual scale at which the global and local structures of a data set can be preserved, and the difference between the two structures be maximized in the feature space, we derived a

  8. Underutilization of high-intensity statin therapy after hospitalization for coronary heart disease.

    Science.gov (United States)

    Rosenson, Robert S; Kent, Shia T; Brown, Todd M; Farkouh, Michael E; Levitan, Emily B; Yun, Huifeng; Sharma, Pradeep; Safford, Monika M; Kilgore, Meredith; Muntner, Paul; Bittner, Vera

    2015-01-27

    National guidelines recommend use of high-intensity statins after hospitalization for coronary heart disease (CHD) events. This study sought to estimate the proportion of Medicare beneficiaries filling prescriptions for high-intensity statins after hospital discharge for a CHD event and to analyze whether statin intensity before hospitalization is associated with statin intensity after discharge. We conducted a retrospective cohort study using a 5% random sample of Medicare beneficiaries between 65 and 74 years old. Beneficiaries were included in the analysis if they filled a statin prescription after a CHD event (myocardial infarction or coronary revascularization) in 2007, 2008, or 2009. High-intensity statins included atorvastatin 40 to 80 mg, rosuvastatin 20 to 40 mg, and simvastatin 80 mg. Among 8,762 Medicare beneficiaries filling a statin prescription after a CHD event, 27% of first post-discharge fills were for a high-intensity statin. The percent filling a high-intensity statin post-discharge was 23.1%, 9.4%, and 80.7%, for beneficiaries not taking statins pre-hospitalization, taking low/moderate-intensity statins, and taking high-intensity statins before their CHD event, respectively. Compared with beneficiaries not on statin therapy pre-hospitalization, multivariable adjusted risk ratios for filling a high-intensity statin were 4.01 (3.58-4.49) and 0.45 (0.40-0.52) for participants taking high-intensity and low/moderate-intensity statins before their CHD event, respectively. Only 11.5% of beneficiaries whose first post-discharge statin fill was for a low/moderate-intensity statin filled a high-intensity statin within 365 days of discharge. The majority of Medicare beneficiaries do not fill high-intensity statins after hospitalization for CHD. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  9. Rising statin use and effect on ischemic stroke outcome

    Directory of Open Access Journals (Sweden)

    Haymore Joseph

    2004-03-01

    Full Text Available Abstract Background Statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors have neuroprotective effects in experimental stroke models and are commonly prescribed in clinical practice. The aim of this study was to determine if patients taking statins before hospital admission for stroke had an improved clinical outcome. Methods This was an observational study of 436 patients admitted to the National Institutes of Health Suburban Hospital Stroke Program between July 2000 and December 2002. Self-reported risk factors for stroke were obtained on admission. Stroke severity was determined by the admission National Institutes of Health Stroke Scale score. Good outcome was defined as a Rankin score Results There were 436 patients with a final diagnosis of ischemic stroke; statin data were available for 433 of them. A total of 95/433 (22% of patients were taking a statin when they were admitted, rising from 16% in 2000 to 26% in 2002. Fifty-one percent of patients taking statins had a good outcome compared to 38% of patients not taking statins (p = 0.03. After adjustment for confounding factors, statin pretreatment was associated with a 2.9 odds (95% CI: 1.2–6.7 of a good outcome at the time of hospital discharge. Conclusions The proportion of patients taking statins when they are admitted with stroke is rising rapidly. Statin pretreatment was significantly associated with an improved functional outcome at discharge. This finding could support the early initiation of statin therapy after stroke.

  10. Statin-related myotoxicity.

    Science.gov (United States)

    Fernandes, Vera; Santos, Maria Joana; Pérez, Antonio

    2016-05-01

    Statin therapy has a very important role in decreasing cardiovascular risk, and treatment non-compliance may therefore be a concern in high cardiovascular risk patients. Myotoxicity is a frequent side effect of statin therapy and one of the main causes of statin discontinuation, which limits effective treatment of patients at risk of or with cardiovascular disease. Because of the high proportion of patients on statin treatment and the frequency of statin-related myotoxicity, this is a subject of concern in clinical practice. However, statin-related myotoxicity is probably underestimated because there is not a gold standard definition, and its diagnosis is challenging. Moreover, information about pathophysiology and optimal therapeutic options is scarce. Therefore, this paper reviews the knowledge about the definition, pathophysiology and predisposing conditions, diagnosis and management of statin-related myotoxicity, and provides a practical scheme for its management in clinical practice. Copyright © 2016 SEEN. Published by Elsevier España, S.L.U. All rights reserved.

  11. Clinical Effectiveness of Statin Therapy After Ischemic Stroke: Primary Results From the Statin Therapeutic Area of the Patient-Centered Research Into Outcomes Stroke Patients Prefer and Effectiveness Research (PROSPER) Study.

    Science.gov (United States)

    O'Brien, Emily C; Greiner, Melissa A; Xian, Ying; Fonarow, Gregg C; Olson, DaiWai M; Schwamm, Lee H; Bhatt, Deepak L; Smith, Eric E; Maisch, Lesley; Hannah, Deidre; Lindholm, Brianna; Peterson, Eric D; Pencina, Michael J; Hernandez, Adrian F

    2015-10-13

    In patients with ischemic stroke, data on the real-world effectiveness of statin therapy for clinical and patient-centered outcomes are needed to better inform shared decision making. Patient-Centered Research Into Outcomes Stroke Patients Prefer and Effectiveness Research (PROSPER) is a Patient-Centered Outcomes Research Institute-funded research program designed with stroke survivors to evaluate the effectiveness of poststroke therapies. We linked data on patients ≥65 years of age enrolled in the Get With The Guidelines-Stroke Registry to Medicare claims. Two-year to postdischarge outcomes of those discharged on a statin versus not on a statin were adjusted through inverse probability weighting. Our coprimary outcomes were major adverse cardiovascular events and home time (days alive and out of a hospital or skilled nursing facility). Secondary outcomes included all-cause mortality, all-cause readmission, cardiovascular readmission, and hemorrhagic stroke. From 2007 to 2011, 77 468 patients who were not taking statins at the time of admission were hospitalized with ischemic stroke; of these, 71% were discharged on statin therapy. After adjustment, statin therapy at discharge was associated with a lower hazard of major adverse cardiovascular events (hazard ratio, 0.91; 95% confidence interval, 0.87-0.94), 28 more home-time days after discharge (PStatin therapy at discharge was not associated with increased risk of hemorrhagic stroke (hazard ratio, 0.94; 95% confidence interval, 0.72-1.23). Among statin-treated patients, 31% received a high-intensity dose; after risk adjustment, these patients had outcomes similar to those of recipients of moderate-intensity statin. In older ischemic stroke patients who were not taking statins at the time of admission, discharge statin therapy was associated with lower risk of major adverse cardiovascular events and nearly 1 month more home time during the 2-year period after hospitalization. © 2015 American Heart Association

  12. Clustering of patients with anemia by data mining approach

    Directory of Open Access Journals (Sweden)

    Khadijeh Dolatshah

    2016-05-01

    Full Text Available Background: Anemia disease is the most common hematological disorder which most often occurs in women. Knowledge discovery from large volumes of data associated with records of the disease can improve medical services quality by data mining The goal of this study was to determining and evaluating the status of anemia using data mining algorithms. Methods: In this applied study, laboratory and clinical data of the patients with anemia were studied in the population of women. The data have been gathered during a year in the laboratory of Imam Hossein and Shohada-ye Haft-e Tir Hospitals which contains 690 records and 15 laboratory and clinical features of anemia. To discover hidden relationships and structures using k-medoids algorithm the patients were clustered. The Silhouette index was used to determine clustering quality. Results: The features of red blood cell (RBC, mean corpuscular hemoglobin (MCH, ferritin, gastrointestinal cancer (GI cancer, gastrointestinal surgery (GI surgery and gastrointestinal infection (GI infection by clustering have been determined as the most important patients’ features. These patients according to their features have been seg-mented to three clusters. First, the patients were clustered according to all features. The results showed that clustering with all features is not suitable because of weak structure of clustering. Then, each time the clustering was performed with different number of features. The silhouette index average is 80 percent that shows clustering quality. Therefore clustering is acceptable and has a strong structure. Conclusion: The results showed that clustering with all features is not suitable because of weak structure. Then, each time the clustering was performed with different number of features. The first cluster contains mild iron deficiency anemia, the second cluster contains severe iron deficiency anemia patients and the third cluster contains patients with other anemia cause.

  13. Data mining techniques for thermophysical properties of refrigerants

    International Nuclear Information System (INIS)

    Kuecueksille, Ecir Ugur; Selbas, Resat; Sencan, Arzu

    2009-01-01

    This study presents ten modeling techniques within data mining process for the prediction of thermophysical properties of refrigerants (R134a, R404a, R407c and R410a). These are linear regression (LR), multi layer perception (MLP), pace regression (PR), simple linear regression (SLR), sequential minimal optimization (SMO), KStar, additive regression (AR), M5 model tree, decision table (DT), M5'Rules models. Relations depending on temperature and pressure were carried out for the determination of thermophysical properties as the specific heat capacity, viscosity, heat conduction coefficient, density of the refrigerants. Obtained model results for every refrigerant were compared and the best model was investigated. Results indicate that use of derived formulations from these techniques will facilitate design and optimize of heat exchangers which is component of especially vapor compression refrigeration system

  14. Explaining and predicting workplace accidents using data-mining techniques

    International Nuclear Information System (INIS)

    Rivas, T.; Paz, M.; Martin, J.E.; Matias, J.M.; Garcia, J.F.; Taboada, J.

    2011-01-01

    Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

  15. Explaining and predicting workplace accidents using data-mining techniques

    Energy Technology Data Exchange (ETDEWEB)

    Rivas, T., E-mail: trivas@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Paz, M., E-mail: mpaz.minas@gmail.co [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Martin, J.E., E-mail: jmartin@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Matias, J.M., E-mail: jmmatias@uvigo.e [Dpto. Estadistica e Investigacion Operativa, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Garcia, J.F., E-mail: jgarcia@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Taboada, J., E-mail: jtaboada@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain)

    2011-07-15

    Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

  16. Healthcare Scheduling by Data Mining: Literature Review and Future Directions

    Directory of Open Access Journals (Sweden)

    Maria M. Rinder

    2012-01-01

    Full Text Available This article presents a systematic literature review of the application of industrial engineering methods in healthcare scheduling, with a focus on the role of patient behavior in scheduling. Nine articles that used mathematical programming, data mining, genetic algorithms, and local searches for optimum schedules were obtained from an extensive search of literature. These methods are new approaches to solve the problems in healthcare scheduling. Some are adapted from areas such as manufacturing and transportation. Key findings from these studies include reduced time for scheduling, capability of solving more complex problems, and incorporation of more variables and constraints simultaneously than traditional scheduling methods. However, none of these methods modeled no-show and walk-ins patient behavior. Future research should include more variables related to patient and/or environment.

  17. Application of data mining techniques for nuclear data and instrumentation

    International Nuclear Information System (INIS)

    Toshniwal, Durga

    2013-01-01

    Data mining is defined as the discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, etc. Data mining thus deals with the discovery of hidden trends and patterns from large quantities of data. The field of data mining is emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. It is an interdisciplinary research area and draws upon several roots, including database systems, machine learning, information systems, statistics and expert systems. Data mining, when performed on time series data, is known as time series data mining (TSDM). A time series is a sequence of real numbers, each number representing a value at a point of time. During the past few years, there has been an explosion of research in the area of time series data mining. This includes attempts to model time series data, to design languages to query such data, and to develop access structures to efficiently process queries on such data. Time series data arises naturally in many real-world applications. Efficient discovery of knowledge through time series data mining can be helpful in several domains such as: Stock market analysis, Weather forecasting etc. An important application area of data mining techniques is in nuclear power plant and related data. Nuclear power plant data can be represented in form of time sequences. Often it may be of prime importance to analyze such data to find trends and anomalies. The general goals of data mining include feature extraction, similarity search, clustering and classification, association rule mining and anomaly

  18. Current treatment of dyslipidaemia: PCSK9 inhibitors and statin intolerance.

    Science.gov (United States)

    Koskinas, Konstantinos; Wilhelm, Matthias; Windecker, Stephan

    2016-01-01

    Statins are the cornerstone of the management of dyslipidaemias and prevention of cardiovascular disease. Although statins are, overall, safe and well tolerated, adverse events can occur and constitute an important barrier to maintaining long-term adherence to statin treatment. In patients who cannot tolerate statins, alternative treatments include switch to another statin, intermittent-dosage regimens and non-statin lipid-lowering medications. Nonetheless, a high proportion of statin-intolerant patients are unable to achieve recommended low-density lipoprotein (LDL) cholesterol goals, thereby resulting in substantial residual cardiovascular risk. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a protease implicated in LDL receptor degradation and plays a central role in cholesterol metabolism. In recent studies, PCSK9 inhibition by means of monoclonal antibodies achieved LDL cholesterol reductions of 50% to 70% across various patient populations and background lipid-lowering therapies, while maintaining a favourable safety profile. The efficacy and safety of the monoclonal antibodies alirocumab and evolocumab were confirmed in statin-intolerant patients, indicating that PCSK9 inhibitors represent an attractive treatment option in this challenging clinical setting. PCSK9 inhibitors recently received regulatory approval for clinical use and may be considered in properly selected patients according to current consensus documents, including patients with statin intolerance. In this review we summarise current evidence regarding diagnostic evaluation of statin-related adverse events, particularly statin-associated muscle symptoms, and we discuss current recommendations on the management of statin-intolerant patients. In view of emerging evidence of the efficacy and safety of PCSK9 inhibitors, we further discuss the role of monoclonal PCSK9 antibodies in the management of statin-intolerant hypercholesterolaemic patients.

  19. Disaster prediction of coal mine gas based on data mining

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Liang-shan; Fu, Gui-xiang [Liaoning Technical University, Fuxin (China)

    2008-09-15

    The technique of data mining was applied to predict gas disasters in view of the characteristics of coal mine gas disasters and feature knowledge based on gas disasters. The rough set theory was used to establish a data mining model of gas disaster prediction, and rough set attributes relations were discussed in a prediction model of gas disaster to supplement the shortages of the rough intensive reduction method by using information entropy criteria. The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application. 7 refs., 11 tabs.

  20. Data Mining at NASA: From Theory to Applications

    Science.gov (United States)

    Srivastava, Ashok N.

    2009-01-01

    This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.

  1. Data mining for the social sciences an introduction

    CERN Document Server

    Attewell, Paul

    2015-01-01

    We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining

  2. Development of an Enhanced Generic Data Mining Life Cycle (DMLC)

    OpenAIRE

    Hofmann, Markus; Tierney, Brendan

    2017-01-01

    Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mini...

  3. The application of data mining to flow cytometry.

    Science.gov (United States)

    Nguyen, Andy N D

    2002-05-01

    Data mining is the process of automating information discovery to detect useful patterns, correlations, and trends. Existing data must be fitted into a representative model from which useful information can be derived through a variety of algorithms. The routine generation of vast amounts of data make flow cytometry a logical target for the application of data mining. This informative unit discusses the steps of the data-mining process using the immunophenotyping of hematologic neoplasms to demonstrate the application. The author describes several types of algorithms and provides a useful resource list of commercially available tools.

  4. BOOK REVIEW EDUCATIONAL DATA MINING: APPLICATIONS AND TRENDS

    Directory of Open Access Journals (Sweden)

    Aylin OZTURK

    2016-04-01

    Full Text Available Educational Data Mining (EDM is a developing field based on data mining techniques. EDM emerged as a combination of areas such as machine learning, statistics, computer science, education, cognitive science, and psychometry. EDM focuses on learner characteristics, behaviors, academic achievements, process of learning, educational functionalities, domain knowledge content, assessments, and applications. Educational data mining is defined by Baker (2010 as ‘‘an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’’. EDM is concerned with improving the learning process and environment.

  5. Statins: antimicrobial resistance breakers or makers?

    Directory of Open Access Journals (Sweden)

    Humphrey H.T. Ko

    2017-10-01

    Full Text Available Introduction The repurposing of non-antibiotic drugs as adjuvant antibiotics may help break antimicrobial resistance (AMR. Statins are commonly prescribed worldwide to lower cholesterol. They also possess qualities of AMR “breakers”, namely direct antibacterial activity, synergism with antibiotics, and ability to stimulate the host immune system. However, statins’ role as AMR breakers may be limited. Their current extensive use for cardiovascular protection might result in selective pressures for resistance, ironically causing statins to be AMR “makers” instead. This review examines statins’ potential as AMR breakers, probable AMR makers, and identifies knowledge gaps in a statin-bacteria-human-environment continuum. The most suitable statin for repurposing is identified, and a mechanism of antibacterial action is postulated based on structure-activity relationship analysis. Methods A literature search using keywords “statin” or “statins” combined with “minimum inhibitory concentration” (MIC was performed in six databases on 7th April 2017. After screening 793 abstracts, 16 relevant studies were identified. Unrelated studies on drug interactions; antifungal or antiviral properties of statins; and antibacterial properties of mevastatin, cerivastatin, antibiotics, or natural products were excluded. Studies involving only statins currently registered for human use were included. Results Against Gram-positive bacteria, simvastatin generally exerted the greatest antibacterial activity (lowest MIC compared to atorvastatin, rosuvastatin, and fluvastatin. Against Gram-negative bacteria, atorvastatin generally exhibited similar or slightly better activity compared to simvastatin, but both were more potent than rosuvastatin and fluvastatin. Discussion Statins may serve as AMR breakers by working synergistically with existing topical antibiotics, attenuating virulence factors, boosting human immunity, or aiding in wound healing. It

  6. Maternal vaccination and preterm birth: using data mining as a screening tool

    DEFF Research Database (Denmark)

    Orozova-Bekkevold, Ivanka; Jensen, Henrik; Stensballe, Lone

    2007-01-01

    Objective The main purpose of this study was to identify possible associations between medicines used in pregnancy and preterm deliveries using data mining as a screening tool. Settings Prospective cohort study. Methods We used data mining to identify possible correlates between preterm delivery...... and medicines used by 92,235 pregnant Danish women who took part in the Danish National Birth Cohort (DNBC). We then evaluated the association between one of the identified exposures (vaccination) and the risk for preterm birth by using logistic regression. The women were classified into groups according...... measure Preterm birth, a delivery occurring before the 259th day of gestation (i.e., less than 37 full weeks). Results Data mining had indicated that maternal vaccination (among other factors) might be related to preterm birth. The following regression analysis showed that, the women who reported being...

  7. Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

    Science.gov (United States)

    Stolzer, Alan J.; Halford, Carl

    2007-01-01

    In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

  8. Statin treatment is associated with reduced toll-like receptor 4 immunohistochemical expression on carotid atherosclerotic plaques: a novel effect of statins.

    Science.gov (United States)

    Katsargyris, Athanasios; Klonaris, Chris; Tsiodras, Sotirios; Bastounis, Elias; Giannopoulos, Athanasios; Theocharis, Stamatios

    2011-12-01

    Toll-like receptor 4 (TLR4) has been recently implicated in inflammatory pathways involved in carotid plaque destabilization. Given that statins have plaque stabilization and inflammation reduction effects, we investigated whether TLR4 expression on carotid atherosclerotic plaques correlates with statin intake. Carotid atherosclerotic plaques were obtained on 140 patients (preoperative statin intake, n = 70). TLR4 immunohistochemical expression was investigated in endothelial cells (ECs), macrophages (MACs) and smooth muscle cells (SMCs) of carotid atheroma. TLR4 positivity, over-expression and intensity of immunostaining were compared in statin versus no-statin users. The results of this study showed that statin users had a significantly lower expression of TLR4 in ECs (P = 0.02, 0.001, 0.006 for TLR4 positivity, increased intensity and over-expression, respectively). Similarly, TLR4 positivity was less pronounced in carotid plaque MACs of statin users (P = 0.03). No carotid specimen with increased EC TLR4 intensity or over-expression was observed among statin users. The prevalence of any cerebrovascular accident was 61.4% in the 'no statin' versus 18.6% in the 'statin' group (odds ratio for statin use: 0.14, 95% CI: 0.07-0.31, P < 0.001). In conclusion, statin treatment is associated with attenuated TLR4 expression on human carotid atherosclerotic plaques and a reduced risk of carotid-related cerebrovascular events. TLR4 may potentially mediate statins' plaque stabilization effects. Further investigation is necessary.

  9. An application of data mining in district heating substations for improving energy performance

    Science.gov (United States)

    Xue, Puning; Zhou, Zhigang; Chen, Xin; Liu, Jing

    2017-11-01

    Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

  10. Donor research and matching system based on data mining in organ transplantation.

    Science.gov (United States)

    Koyuncugil, Ali Serhan; Ozgulbas, Nermin

    2010-06-01

    It is very important to identify the appropriate donor in organ transplantation under the time constraint. Clearly, adequate time must be spent in appropriate donor research in that kind of vital operation. On the other hand, time is very important to search for other alternatives in case of inappropriate donor. However, the possibility for determining the most probable donors as fast as possible has an great importance in using time efficiently. From this point view, the main objective of this paper is developing a system which provides probabilistic prior information in donor transplantation via data mining. While the sytem development process, the basic element is the data of successful organ transplantations. Then, the hidden information and patterns will be discovered from this data. Therefore, this process requires the data mining methods from its definition. In this study, an appropriate donor detection system design based on data mining is suggested.

  11. Controlling Cholesterol with Statins

    Science.gov (United States)

    ... For Consumers Home For Consumers Consumer Updates Controlling Cholesterol with Statins Share Tweet Linkedin Pin it More ... not, the following tips can help keep your cholesterol in check: Talk with your healthcare provider about ...

  12. Statins: pros and cons.

    Science.gov (United States)

    Pinal-Fernandez, Iago; Casal-Dominguez, Maria; Mammen, Andrew L

    2017-12-29

    Statins inhibit the critical step of cholesterol synthesis in which 3-hydroxy-3-methylglutaryl coenzyme A (HMGC) is transformed to mevalonate by the enzyme HMGC reductase. By doing so, they have a potent lipid-lowering effect that reduces cardiovascular risk and decreases mortality. Since the mevalonate pathway also influences endothelial function, the inflammatory response, and coagulation, the effects of statins reach well beyond their cholesterol lowering properties. As with all drugs, statins may have adverse effects; these include musculoskeletal symptoms, increased risk of diabetes, and higher rates of hemorrhagic stroke. However, the frequency of adverse effects is extremely low and, in selected patient populations, the benefits of statins considerably outweigh the potential risks. Published by Elsevier España, S.L.U.

  13. How to take statins

    Science.gov (United States)

    ... Raising HDL (good) cholesterol in your blood Lowering triglycerides , another type of fat in your blood Statins ... gov/pubmed/26299317 . Semenkovich CF Disorders of lipid metabolism. In: Goldman L, Schafer AI, eds. Goldman's Cecil ...

  14. Data mining of air traffic control operational errors

    Science.gov (United States)

    2006-01-01

    In this paper we present the results of : applying data mining techniques to identify patterns and : anomalies in air traffic control operational errors (OEs). : Reducing the OE rate is of high importance and remains a : challenge in the aviation saf...

  15. Visual Data Mining of Robot Performance Data, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to design and develop VDM/RP, a visual data mining system that will enable analysts to acquire, store, query, analyze, and visualize recent and historical...

  16. 2nd International Conference on Computational Intelligence in Data Mining

    CERN Document Server

    Mohapatra, Durga

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 – 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

  17. Data Mining for IVHM using Sparse Binary Ensembles, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — In response to NASA SBIR topic A1.05, "Data Mining for Integrated Vehicle Health Management", Michigan Aerospace Corporation (MAC) asserts that our unique SPADE...

  18. Transparent data mining for big and small data

    CERN Document Server

    Quercia, Daniele; Pasquale, Frank

    2017-01-01

    This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to prac...

  19. Usage of Data Mining at Financial Decision Making

    Directory of Open Access Journals (Sweden)

    Levent BORAN

    2014-06-01

    Full Text Available The knowledge age requires controlling every kind of information. Recognition of patterns in data may provide previously unknown and useful information that can provide competitive advantages. If related techniques are applied on financial statements, it is possible to acquire valuable information about companies’ financial situations. It is considered that data mining could be an alternative of common financial analysis techniques such as vertical analysis, horizontal analysis, trend analysis and ratio analysis. Against existing financial analysis methods, data mining provides some advantages, which are ability of manipulation of huge data and competence of obtaining previously unknown information. There exist two major constraints of data mining implementation that are lack of experts on both data mining and related domains and cost of computer software and hardware used.

  20. artery disease guidelines with extracted knowledge from data mining

    Directory of Open Access Journals (Sweden)

    Peyman Rezaei-Hachesu

    2017-06-01

    Conclusion: Guidelines confirm the achieved results from data mining (DM techniques and help to rank important risk factors based on national and local information. Evaluation of extracted rules determined new patterns for CAD patients.

  1. Accounting and Financial Data Analysis Data Mining Tools

    Directory of Open Access Journals (Sweden)

    Diana Elena Codreanu

    2011-05-01

    Full Text Available Computerized accounting systems in recent years have seen an increase in complexity due to thecompetitive economic environment but with the help of data analysis solutions such as OLAP and DataMining can be a multidimensional data analysis, can detect the fraud and can discover knowledge hidden indata, ensuring such information is useful for decision making within the organization. In the literature thereare many definitions for data mining but all boils down to same idea: the process takes place to extract newinformation from large data collections, information without the aid of data mining tools would be verydifficult to obtain. Information obtained by data mining process has the advantage that only respond to thequestion of what happens but at the same time argue and show why certain things are happening. In this paperwe wish to present advanced techniques for analysis and exploitation of data stored in a multidimensionaldatabase.

  2. Application of Data Mining for Card Fraud Detection

    Directory of Open Access Journals (Sweden)

    I.V. Andrianov

    2012-03-01

    Full Text Available This paper focuses on implementing Data Mining methods for card fraud detection. The approach to classification and prediction tasks for detection of unauthorized transactions is considered.

  3. Data mining in e-commerce: A survey

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... Data mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Our intent is not to ...

  4. Discrimination Discovery and Prevention in Data Mining: A Survey

    OpenAIRE

    Jagriti Singh; Prof. Dr. S. S. Sane

    2014-01-01

    Data Mining is the computation process of discovering knowledge or patterns in large data sets. But extract knowledge without violation such as privacy and non-discrimination is most difficult and challenging. This is mainly because of data mining techniques such as classification rules are actually learned by the system from the training data and training data sets itself are biased in what regards discriminatory (sensitive) attributes like gender, race, religion, etc. As a r...

  5. Application of Rough Set Theory in Data Mining

    OpenAIRE

    Slimani, Thabet

    2013-01-01

    Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. Data mining is a discipline that has an important contribution to data analysis, discovery of new meaningful knowledge, and autonomous decision making. The rough set theory offers a viable approach for decision rule extraction from data.This paper, introduces the fundamental concepts of rough set theory and other aspects of data mining, a discussion of data representation with rough set t...

  6. ANALISA ASOSIATIF DATA MINING UNTUK MENGETAHUI POLA KECELAKAAN LALU LINTAS

    OpenAIRE

    Agus Sasmito Aribowo

    2015-01-01

    The data of traffic accident can be processed into information that is important for Police Department. Those important information researched is to analyze the traffic accident data to find out is there any link between the occurrence of an accident to a certain brand of vehicle. This research implementing data mining method to process the data traffic accident by using data mining techniques called Apriori Method. Apriori Method is used to identify a pattern of accidents based on brand,...

  7. Combining complex networks and data mining: why and how

    OpenAIRE

    Zanin, M.; Papo, D.; Sousa, P. A.; Menasalvas, E.; Nicchi, A.; Kubik, E.; Boccaletti, S.

    2016-01-01

    The increasing power of computer technology does not dispense with the need to extract meaningful in- formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extr...

  8. An Intelligent Agent based Architecture for Visual Data Mining

    OpenAIRE

    Hamdi Ellouzi; Hela Ltifi; Mounir Ben Ayed

    2016-01-01

    the aim of this paper is to present an intelligent architecture of Decision Support System (DSS) based on visual data mining. This architecture applies the multi-agent technology to facilitate the design and development of DSS in complex and dynamic environment. Multi-Agent Systems add a high level of abstraction. To validate the proposed architecture, it is implemented to develop a distributed visual data mining based DSS to predict nosocomial infectionsoccurrence in intensive care units. Th...

  9. [Aspects for data mining implementation in gerontology and geriatrics].

    Science.gov (United States)

    Mikhal'skiĭ, A I

    2014-01-01

    Current challenges facing theory and practice in ageing sciences need new methods of experimental data investigation. This is a result as of experimental basis developments in biological research, so of information technology progress. These achievements make it possible to use well proven in different fields of science and engineering data mining methods for tasks in gerontology and geriatrics. Some examples of data mining methods implementation in gerontology are presented.

  10. Scalable, distributed data mining using an agent based architecture

    Energy Technology Data Exchange (ETDEWEB)

    Kargupta, H.; Hamzaoglu, I.; Stafford, B.

    1997-05-01

    Algorithm scalability and the distributed nature of both data and computation deserve serious attention in the context of data mining. This paper presents PADMA (PArallel Data Mining Agents), a parallel agent based system, that makes an effort to address these issues. PADMA contains modules for (1) parallel data accessing operations, (2) parallel hierarchical clustering, and (3) web-based data visualization. This paper describes the general architecture of PADMA and experimental results.

  11. Predictive models in churn data mining: a review

    OpenAIRE

    García, David L.; Vellido Alcacena, Alfredo; Nebot Castells, M. Àngela

    2007-01-01

    The development of predictive models of customer abandonment plays a central role in any churn management strategy. These models can be developed using either qualitative approaches or can take a data-centred point of view. In the latter case, the use of Data Mining procedures and techniques can provide useful and actionable insights into the processes leading to abandonment. In this report, we provide a brief and structured review of some of the Data Mining approaches that have been put forw...

  12. Data mining in e-commerce: A survey

    Indian Academy of Sciences (India)

    Data mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Our intent is not to survey the plethora ...

  13. DECISION SUPPORT SYSTEM TO SUPPORT DECISION PROCESSES WITH DATA MINING

    OpenAIRE

    Rupnik, Rok; Kukar, Matjaž

    2007-01-01

    Traditional techniques of data analysis do not enable the solution of all kind of problems and for that reason they have become insufficient. This caused a newinterdisciplinary field of data mining to arise, encompassing both classical statistical, and modern machine learning techniques to support the data analysis and knowledge discovery from data. Data mining methods are powerful in dealing with large quantities of data, but on the other hand they are difficult to master by business users t...

  14. Impact of statins on progression of atherosclerosis: rationale and design of SATURN (Study of Coronary Atheroma by InTravascular Ultrasound: effect of Rosuvastatin versus AtorvastatiN).

    Science.gov (United States)

    Nicholls, Stephen J; Borgman, Marilyn; Nissen, Steven E; Raichlen, Joel S; Ballantyne, Christie; Barter, Philip; Chapman, M John; Erbel, Raimund; Libby, Peter

    2011-06-01

    Previous imaging studies have demonstrated that the beneficial impact of high-dose statins on the progression of coronary atherosclerosis associates with their ability to lower levels of low-density lipoprotein cholesterol (LDL-C) and C-reactive protein (CRP) and to raise high-density lipoprotein cholesterol (HDL-C). The Study of Coronary Atheroma by InTravascular Ultrasound: Effect of Rosuvastatin versus AtorvastatiN (SATURN, NCT00620542) aims to compare the effects of high-dose atorvastatin and rosuvastatin on disease progression. A total of 1385 subjects with established coronary artery disease (CAD) on angiography were randomized to receive rosuvastatin 40 mg or atorvastatin 80 mg for 24 months. The primary efficacy parameter will be the nominal change in percent atheroma volume (PAV), determined by analysis of intravascular ultrasound (IVUS) images of matched coronary artery segments acquired at baseline and at 24-month follow-up. The effect of statin therapy on plasma lipids and inflammatory markers, and the incidence of clinical cardiovascular events will also be assessed. The study does not have the statistical power to directly compare the treatment groups with regard to clinical events. Serial IVUS has emerged as a sensitive imaging modality to assess the impact of treatments on arterial structure. In this study, IVUS will be used to determine whether high-dose statins have different effects on plaque progression.

  15. Analisis Data Lulusan dengan Data Mining untuk Mendukung Strategi Promosi Universitas Lancang Kuning

    Directory of Open Access Journals (Sweden)

    Elvira Asril

    2015-11-01

    Full Text Available Setiap perusahaan maupun organisasi yang ingin tetap bertahan perlu untuk menentukan strategi promosi yang tepat. Penentuan strategi promosi yang tepat akan dapat mengurangi biaya promosi dan mencapai sasaran promosi yang tepat. Salah satu cara yang dapat dilakukan untuk penentuan strategi promosi adalah dengan menggunakan teknik data mining. Teknik data mining yang digunakan dalam hal ini adalah dengan menggunakan algoritma Clustering K-Means. Clustering merupakan pengelompokkan record, observasi, atau kasus ke dalam kelas-kelas objek yang mirip. K-Means adalah metode klaster data non-hirarkis yang mencoba untuk membagi data ke dalam satu atau lebih klaster. Penelitian dilakukan dengan mengamati beberapa variabel penelitian yang sering dipertimbangkan oleh perguruan tinggi dalam menentukan sasaran promosinya yaitu asal sekolah, daerah, dan jurusan. Hasil penelitian ini adalah berupa pola menarik hasil data mining yang merupakan informasi penting untuk mendukung strategi promosi yang tepat dalam mendapatkan calon mahasiswa baru.Kata kunci: Data Mining, Clustering, K-Means Each company or organization that wants to survive needs to determine appropriate promotional strategies. Determination of appropriate promotional strategies will be able to reduce costs and achieve the goals the promotion of proper promotion. One way that can be done to determine campaign strategy is to use data mining techniques. Data mining techniques used in this case is to use a K-Means clustering algorithm. Clustering is the grouping of records, observation, or in the case of the object classes that are similar. K-Means is a method of non-hierarchical clustering of data that is trying to divide the data into one or more clusters. The study was conducted by observing some of the variables that are often considered by the college in determining the target of promotion that the school of origin, region, and department. Results of this study are interesting pattern of

  16. Statin use in adults at high risk of cardiovascular disease mortality: cross-sectional analysis of baseline data from The Irish Longitudinal Study on Ageing (TILDA)

    Science.gov (United States)

    Murphy, Catriona; Bennett, Kathleen; Fahey, Tom; Shelley, Emer; Graham, Ian; Kenny, Rose Anne

    2015-01-01

    Objectives This study aims to examine the extent to which statins are used by adults at high risk of cardiovascular disease (CVD) compared to European clinical guidelines. The high-risk groups examined are those with (1) known CVD, (2) known diabetes and (3) a high or very high risk (≥5%) of CVD mortality based on Systematic COronary Risk Evaluation (SCORE). Design This study is cross-sectional in design using data from the first wave (2009–2011) of The Irish Longitudinal Study on Ageing (TILDA). Setting and participants The sample (n=3372) is representative of community living adults aged 50–64 years in Ireland. Results Statins were used by 68.6% (95% CI 61.5% to 75.8%) of those with known CVD, 57.4% (95% CI 49.1% to 65.7%) of those with known diabetes and by 19.7% (95% CI 13.0% to 26.3%) of adults with a SCORE risk ≥5%. Over a third (38.5%, 95% CI 31.0% to 46.0%) of those with known CVD, 46.8% (95% CI 38.4% to 55.1%) of those with known diabetes and 85.2% (95% CI 79.3% to 91.1%) of those with a SCORE risk ≥5% were at or above the low-density lipoprotein cholesterol (LDL-C) target of 2.5 mmol/L specified in the 2007 European guidelines. Conclusions Despite strong evidence and clinical guidelines recommending the use of statins for secondary prevention, a gap exists between guidelines and practice in this cohort. It is also of concern that a low proportion of adults with a SCORE risk ≥5% were taking statins. A policy response that strengthens secondary prevention, and improves risk assessment and shared decision-making in the primary prevention of CVD is required. PMID:26169806

  17. Perioperative Statin Therapy Is Not Associated With Reduced Risk of Anastomotic Leakage After Colorectal Resection

    DEFF Research Database (Denmark)

    Bisgård, Anne Sofie; Noack, Morten Westergaard; Klein, Mads

    2013-01-01

    Anastomotic leakage is a serious complication of colorectal surgery. Several studies have demonstrated the beneficial pleiotropic effects of statins, and preliminary studies have suggested that perioperative statin treatment may be associated with reduced risk of anastomotic leakage....

  18. The effects of 2 weeks of statin treatment on mitochondrial respiratory capacity in middle-aged males: the LIFESTAT study.

    Science.gov (United States)

    Asping, Magnus; Stride, Nis; Søgaard, Ditte; Dohlmann, Tine Lovsø; Helge, Jørn W; Dela, Flemming; Larsen, Steen

    2017-06-01

    Statins are used to lower cholesterol in plasma and are one of the most used drugs in the world. Many statin users experience muscle pain, but the mechanisms are unknown at the moment. Many studies have hypothesized that mitochondrial function could be involved in these side effects. The aim of the study was to investigate mitochondrial function after 2 weeks of treatment with simvastatin (S; n = 10) or pravastatin (P; n = 10) in healthy middle-aged participants. Mitochondrial respiratory capacity and substrate sensitivity were measured in permeabilized muscle fibers by high-resolution respirometry. Mitochondrial content (citrate synthase (CS) activity), antioxidant content, as well as coenzyme Q 10 concentration (Q 10 ) were determined. Fasting plasma glucose and insulin concentrations were measured, and whole body maximal oxygen uptake (VO 2max ) was determined. No differences were seen in mitochondrial respiratory capacity although a tendency was observed for a reduction when complex IV respiration was analyzed in both S (229 (169; 289 (95% confidence interval)) vs. 179 (146; 211) pmol/s/mg, respectively; P = 0.062) and P (214 (143; 285) vs. 162 (104; 220) pmol/s/mg, respectively; P = 0.053) after treatment. A tendency (1.64 (1.28; 2.00) vs. 1.28 (0.99; 1.58) mM, respectively; P = 0.092) for an increased mitochondrial substrate sensitivity (complex I-linked substrate; glutamate) was seen only in S after treatment. No differences were seen in Q 10 , CS activity, or antioxidant content after treatment. Fasting glucose and insulin as well as VO 2max were not changed after treatment. Two weeks of statin (S or P) treatment have no major effect on mitochondrial function. The tendency for an increased mitochondrial substrate sensitivity after simvastatin treatment could be an early indication of the negative effects linked to statin treatment.

  19. Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

    Science.gov (United States)

    Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic

    2014-01-01

    This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…

  20. A Meta-Analysis of Educational Data Mining on Improvements in Learning Outcomes

    Science.gov (United States)

    AlShammari, Iqbal A.; Aldhafiri, Mohammed D.; Al-Shammari, Zaid

    2013-01-01

    A meta-synthesis study was conducted of 60 research studies on educational data mining (EDM) and their impacts on and outcomes for improving learning outcomes. After an overview, an examination of these outcomes is provided (Romero, Ventura, Espejo, & Hervas, 2008; Romero, "et al.", 2011). Then, a review of other EDM-related research…

  1. Statin treatment and risk of recurrent venous thromboembolism

    DEFF Research Database (Denmark)

    Nguyen, Cu Dinh; Andersson, Charlotte; Jensen, Thomas Bo

    2013-01-01

    Objectives Statins may decrease the risk of primary venous thromboembolism (VTE), that is, deep vein thrombosis (DVT) and pulmonary embolism (PE) but the effect of statins in preventing recurrent VTE is less clear. The aim of this study was therefore to investigate the association between statin...... hospitalised VTE (ie, fatal or non-fatal DVT or PE) associated with use of statins. Results 44330 patients with VTE were included in the study. Of these 3914 were receiving statin therapy at baseline. Patients receiving statins were older (6811 compared to 62 +/- 18years), had more comorbidity and used more...... medications. The incidence rate for recurrent VTE was 24.4 (95% CI 22.8 to 26.2) per 1000 person-years among statin users and 48.5 (95% CI 47.4 to 49.7) per 1000 person-years among non-statin users. Statin use was associated with a significantly lower risk of a recurrent VTE, adjusted HR 0.74 (95% CI 0...

  2. Statin utilization according to indication and age: A Danish cohort study on changing prescribing and purchasing behaviour

    DEFF Research Database (Denmark)

    Kildemoes, Helle Wallach; Vass, Mikkel; Hendriksen, Carsten

    2012-01-01

    Objective: Introduced to reduce mortality after myocardial infarction (MI), statins are now recommended for a range of other conditions, including asymptomatic individuals without cardiovascular disease or diabetes. The aim was to describe trends in Danish statin utilization according to indication...... and age during 1996-2009, and to analyse changing prescribing and purchasing behaviour during time intervals (driver periods) a priori defined by potential influential factors. Methods: A nationwide cohort (N = 4,998,580) was followed in Danish individual-level registries. Based on a hierarchy of register...

  3. Possible association of ABCB1:c.3435T>C polymorphism with high-density-lipoprotein-cholesterol response to statin treatment - a pilot study.

    Directory of Open Access Journals (Sweden)

    Anna Sałacka

    2014-08-01

    Full Text Available The gene product ABCB1 (formerly MDR1 or P-glycoprotein is hypothesized to be involved in cholesterol cellular trafficking, redistribution and intestinal re-absorption. Carriers of the ABCB1:3435T allele have previously been associated with decreases in ABCB1 mRNA and protein concentrations and have been correlated with changes in serum lipid concentrations. The aim of this study was to investigate possible association between the ABCB1:3435T>C polymorphism and changes in lipids in patients following statin treatment. Outpatients (n=130 were examined: 43 men (33%, 87 women (67%: treated with atorvastatin or simvastatin (all patients with equivalent dose of 20 or 40 mg/d simvastatin. Blood was taken for ABCB1:3435T>C genotyping, and before and after statin treatment for lipid concentration determination (total cholesterol, high-density-lipoprotein-cholesterol (HDL-C, triglycerides. Change (Δ in lipid parameters, calculated as differences between measurements before and after treatment, were analyzed with multiple regression adjustments: gender, diabetes, age, body mass index, equivalent statin dose, length of treatment. Univariate and multivariate analyses showed significant differences in ΔHDL-C (univariate p=0.029; multivariate p=0.036 and %ΔHDL-C (univariate p=0.021; multivariate p=0.023 between patients with TT (-0.05 ± 0.13 g/l; -6.8% ± 20%; respectively and CC+CT genotypes (0.004 ± 0.15 g/l; 4.1 ± 26%; respectively. Reduction of HDL-C in homozygous ABCB1:3435TT patients suggests this genotype could be associated with a reduction in the benefits of statin treatment.

  4. Statin Intolerance: A Literature Review and Management Strategies.

    Science.gov (United States)

    Saxon, David R; Eckel, Robert H

    Statin intolerance is a commonly encountered clinical problem for which useful management strategies exist. Although many patients report statin-related muscle symptoms, studies indicate that the majority of these patients can tolerate a statin upon re-challenge. Alternative statin dosing strategies are an effective way to modify and reintroduce statin therapy for patients reporting adverse symptoms. Correction of vitamin D deficiency and hypothyroidism may improve statin tolerability in some patients. CoQ10 supplementation has been found to be of no benefit for statin-related muscle symptoms in most recent clinical trials. PCSK9 inhibitors are a new therapeutic option that if confirmed as safe and effective by outcomes trials may be of substantial benefit to select patients at high ASCVD risk who are unable to achieve adequate low-density lipoprotein cholesterol (LDL-C) lowering on maximally tolerated statin therapy. Other available medications to lower LDL-C in statin intolerant patients include ezetimibe, bile acid sequestrants, niacin, and fibrates. Published by Elsevier Inc.

  5. Race-Sex Differences in Statin Use and Low-Density Lipoprotein Cholesterol Control Among People With Diabetes Mellitus in the Reasons for Geographic and Racial Differences in Stroke Study.

    Science.gov (United States)

    Gamboa, Christopher M; Colantonio, Lisandro D; Brown, Todd M; Carson, April P; Safford, Monika M

    2017-05-10

    Statin therapy is a cornerstone of cardiovascular disease risk reduction for people with diabetes mellitus. Past reports have shown race-sex differences in statin use in general populations, but statin patterns by race and sex in those with diabetes mellitus have not been thoroughly studied. Our sample of 4288 adults ≥45 years of age with diagnosed diabetes mellitus who had low-density lipoprotein cholesterol (LDL-C) >100 mg/dL or were taking statins recruited for the Reasons for Geographic and Racial Differences in Stroke study from 2003 to 2007. Exposures included race-sex groups (white men [WM], black men [BM], white women [WW], black women [BW]) and factors that may influence healthcare utilization. Proportions and prevalence ratios were calculated for statin use and LDL-C control. Statin use for WM, BM, WW, and BW was 66.0%, 57.8%, 55.0%, and 53.6%, respectively ( P diabetes mellitus. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

  6. Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

    Science.gov (United States)

    Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

    2016-01-01

    The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

  7. Building a Bridge or Digging a Pipeline? Clinical Data Mining in Evidence-Informed Knowledge Building

    Science.gov (United States)

    Epstein, Irwin

    2015-01-01

    Challenging the "bridge metaphor" theme of this conference, this article contends that current practice-research integration strategies are more like research-to-practice "pipelines." The purpose of this article is to demonstrate the potential of clinical data-mining studies conducted by practitioners, practitioner-oriented PhD…

  8. Exploring Student Characteristics of Retention That Lead to Graduation in Higher Education Using Data Mining Models

    Science.gov (United States)

    Raju, Dheeraj; Schumacker, Randall

    2015-01-01

    The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

  9. Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

    Science.gov (United States)

    Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

    2017-01-01

    In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

  10. Geospatiotemporal data mining in an early warning system for forest threats in the United States

    Science.gov (United States)

    F.M. Hoffman; R.T. Mills; J. Kumar; S.S. Vulli; W.W. Hargrove

    2010-01-01

    We investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250 m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an early warning system to identify threats to forest ecosystems. Cluster...

  11. Application of Learning Analytics Using Clustering Data Mining for Students' Disposition Analysis

    Science.gov (United States)

    Bharara, Sanyam; Sabitha, Sai; Bansal, Abhay

    2018-01-01

    Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research…

  12. Data Mining for Web Site Evaluation: An Exploration of Site Usage by Graduate Social Work Students

    Science.gov (United States)

    Santhiveeran, Janaki

    2006-01-01

    This paper evaluates the actual use of a course Website by graduate social work students. The study utilized data mining techniques to discover meaningful trends by using the data from server logs. The course Website was accessed 24,730 times by all 49 graduate students during a semester. The students utilized the course Website 23 hours a day, 7…

  13. Statins for aortic valve stenosis.

    Science.gov (United States)

    Thiago, Luciana; Tsuji, Selma Rumiko; Nyong, Jonathan; Puga, Maria Eduarda Dos Santos; Góis, Aécio Flávio Teixeira de; Macedo, Cristiane Rufino; Valente, Orsine; Atallah, Álvaro Nagib

    2016-01-01

    Aortic valve stenosis is the most common type of valvular heart disease in the USA and Europe. Aortic valve stenosis is considered similar to atherosclerotic disease. Some studies have evaluated statins for aortic valve stenosis. To evaluate the effectiveness and safety of statins in aortic valve stenosis. Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, LILACS - IBECS, Web of Science and CINAHL Plus. These databases were searched from their inception to 24 November 2015. We also searched trials in registers for ongoing trials. We used no language restrictions.Selection criteria: Randomized controlled clinical trials (RCTs) comparing statins alone or in association with other systemic drugs to reduce cholesterol levels versus placebo or usual care. Data collection and analysis: Primary outcomes were severity of aortic valve stenosis (evaluated by echocardiographic criteria: mean pressure gradient, valve area and aortic jet velocity), freedom from valve replacement and death from cardiovascular cause. Secondary outcomes were hospitalization for any reason, overall mortality, adverse events and patient quality of life.Two review authors independently selected trials for inclusion, extracted data and assessed the risk of bias. The GRADE methodology was employed to assess the quality of result findings and the GRADE profiler (GRADEPRO) was used to import data from Review Manager 5.3 to create a 'Summary of findings' table. We included four RCTs with 2360 participants comparing statins (1185 participants) with placebo (1175 participants). We found low-quality evidence for our primary outcome of severity of aortic valve stenosis, evaluated by mean pressure gradient (mean difference (MD) -0.54, 95% confidence interval (CI) -1.88 to 0.80; participants = 1935; studies = 2), valve area (MD -0.07, 95% CI -0.28 to 0.14; participants = 127; studies = 2), and aortic jet velocity (MD -0.06, 95% CI -0.26 to 0

  14. Pleiotropic effects of statins

    Directory of Open Access Journals (Sweden)

    Narasaraju Kavalipati

    2015-01-01

    Full Text Available Statins or 3-hydroxy-methylglutaryl coenzyme A (HMG CoA reductase inhibitors not only prevents the synthesis of cholesterol biosynthesis but also inhibits the synthesis of essential isoprenoid intermediates such as farnesyl pyrophosphate, geranylgeranyl pyrophosphate, isopentanyl adenosine, dolichols and polyisoprenoid side chains of ubiquinone, heme A, and nuclear lamins. These isoprenoid intermediates are required for activation of various intracellular/signaling proteins- small guanosine triphosphate bound protein Ras and Ras-like proteins like Rho, Rab, Rac, Ral, or Rap which plays an indispensible role in multiple cellular processes. Reduction of circulating isoprenoids intermediates as a result of HMG CoA reductase inhibition by statins prevents activation of these signalling proteins. Hence, the multiple effects of statins such as antiinflammatory effects, antioxidant effects, antiproliferative and immunomodulatory effects, plaque stability, normalization of sympathetic outflow, and prevention of platelet aggregation are due to reduction of circulating isoprenoids and hence inactivation of signalling proteins. These multiple lipid-independent effects of statins termed as statin pleiotropy would potentially open floodgates for research in multiple treatment domains catching attentions of researchers and clinician across the globe.

  15. Statins use and risk of new-onset diabetes in hypertensive patients: a population-based retrospective cohort study in Yinzhou district, Ningbo city, People’s Republic of China

    Directory of Open Access Journals (Sweden)

    Li H

    2018-05-01

    Full Text Available Hailong Li,1 Hongbo Lin,2 Houyu Zhao,1 Yang Xu,1 Yinchu Cheng,1 Peng Shen,2 Siyan Zhan1 1Department of Epidemiology and Bio-statistics, School of Public Health, Peking University Health Science Centre, Beijing, People’s Republic of China; 2Department of Chronic Diseases and Health Promotion, Yinzhou District Center for Disease Control and Prevention, Ningbo, People’s Republic of China Background: Reports have suggested that statin use is associated with an increased incidence of type 2 diabetes mellitus (T2DM. Guidelines suggested that statins should be prescribed in hypertensive patients for primary prevention. However, there were very few studies on the risk of T2DM associated with statin use among patients with hypertension in mainland People’s Republic of China. Purpose: To determine the association between statin use and new-onset diabetes mellitus among patients with hypertension in mainland People’s Republic of China. Patients and methods: We performed a retrospective cohort study of hypertensive patients using the Yinzhou regional health care database from January 1, 2010, to August 31, 2016. Patients aged 30–90 years old without T2DM were eligible for inclusion. We identified new statin initiators and nonusers by using prescription records of inpatients and outpatients. Multivariate Cox model and propensity score methods were used to adjust potential confounders, including age, sex, body mass index, comorbidities, lifestyle characteristics, and baseline antihypertensive drug use. The risk of incident T2DM among statin initiators compared to nonusers was estimated by the Cox proportional hazards model. Propensity scores for statin use were then developed using logistic regression, statin initiators were matched 1:1 with nonusers according to propensity scores with the nearest neighbor matching method within 0.2 caliper width, and Cox regression was again conducted. Results: Among 67,993 patients (21,551 statin initiators; 46

  16. Event metadata records as a testbed for scalable data mining

    International Nuclear Information System (INIS)

    Gemmeren, P van; Malon, D

    2010-01-01

    At a data rate of 200 hertz, event metadata records ('TAGs,' in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise 'data mining,' but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.

  17. Data mining in healthcare: decision making and precision

    Directory of Open Access Journals (Sweden)

    Ionuţ ŢĂRANU

    2016-05-01

    Full Text Available The trend of application of data mining in healthcare today is increased because the health sector is rich with information and data mining has become a necessity. Healthcare organizations generate and collect large volumes of information to a daily basis. Use of information technology enables automation of data mining and knowledge that help bring some interesting patterns which means eliminating manual tasks and easy data extraction directly from electronic records, electronic transfer system that will secure medical records, save lives and reduce the cost of medical services as well as enabling early detection of infectious diseases on the basis of advanced data collection. Data mining can enable healthcare organizations to anticipate trends in the patient's medical condition and behaviour proved by analysis of prospects different and by making connections between seemingly unrelated information. The raw data from healthcare organizations are voluminous and heterogeneous. It needs to be collected and stored in organized form and their integration allows the formation unite medical information system. Data mining in health offers unlimited possibilities for analyzing different data models less visible or hidden to common analysis techniques. These patterns can be used by healthcare practitioners to make forecasts, put diagnoses, and set treatments for patients in healthcare organizations.

  18. AN EFFICIENT APPROACH FOR DETECTION OF HEART ATTACK USING NOBLE ANT COLONY OPTIMIZATION CONCEPT OF DATA MINING

    OpenAIRE

    Pise Satish Prakashrao*1, Anoop Singh 2 & Ritesh Kumar Yadav3

    2018-01-01

    The goal of data mining is to extract knowledge from large amounts of data. Data Mining is an interdisciplinary field that focuses on machine learning, statistics and databases. In this article, we highlight a new framework that uses a combination of data extraction and ant colony optimization to collect heart disease such as early heart attacks to protect them and reduce mortality rates. This study focused on the formulation and implementation of an improved and reliable model for the diagno...

  19. Statin treatment and mortality in community-dwelling frail older patients with diabetes mellitus : A retrospective observational study

    NARCIS (Netherlands)

    A. Pilotto (Alberto); F. Panza (Francesco); Copetti, M. (Massimiliano); Simonato, M. (Matteo); D. Sancarlo; P. Gallina (Pietro); T.E. Strandberg (Timo); A.J. Cruz-Jentoft (A.); Daragjati, J. (Julia); L. Ferrucci (Luigi); A. Fontana (Andrea); S. Maggi; F.U.S. Mattace Raso (Francesco); M. Paccalin; Polidori, M.C. (Maria Cristina); Schulz, R.-J. (Ralf-Joachim); E. Topinkova; G. Trifirò (Gianluca); A.-K. Welmer

    2015-01-01

    textabstractBackground: Older adults are often excluded from clinical trials. Decision making for administration of statins to older patients with diabetes mellitus (DM) is under debate, particularly in frail older patients with comorbidity and high mortality risk. We tested the hypothesis that

  20. Statin Use and Cognitive Function : Population-Based Observational Study with Long-Term Follow-Up

    NARCIS (Netherlands)

    Joosten, Hanneke; Visser, Sipke T.; van Eersel, Marlise E.; Gansevoort, Ron T.; Bilo, Henk J. G.; Slaets, Joris P.; Izaks, Gerbrand J.

    2014-01-01

    We aimed to evaluate the association between statin use and cognitive function. Cognitive function was measured with the Ruff Figural Fluency Test (RFFT; worst score, 0; best score, 175 points) and the Visual Association Test (VAT; low performance, 0-10; high performance, 11-12 points) in an

  1. Effect of statin use on mobility disability and its prevention in at-risk older adults: the LIFE study

    Science.gov (United States)

    BACKGROUND: HMG-CoA reductase inhibitors (statins) are among the most commonly prescribed classes of medications. Although their cardiovascular benefits and myalgia risks are well documented, their effects on older adults initiating an exercise training program are less understood. METHODS: 1,635 s...

  2. Efficacy of Local and Systemic Statin Delivery on the Osseointegration of Implants: A Systematic Review.

    Science.gov (United States)

    Kellesarian, Sergio Varela; Al Amri, Mohammad D; Al-Kheraif, Abdulaziz A; Ghanem, Alexis; Malmstrom, Hans; Javed, Fawad

    In indexed literature, a systematic review of the efficacy of statins in enhancing osseointegration is lacking. The aim of this systematic review was to assess the efficacy of local and systemic statin delivery on the osseointegration of implants. To address the focused question, "Does local and systemic statin delivery affect osseointegration around implants?", indexed databases were searched from 1965 through November 2015 using various keywords. Letters to the Editor, case reports/case series, historic reviews, and commentaries were excluded. The pattern of this systematic review was customized to primarily summarize the pertinent data. Nineteen studies were included. All studies were experimental and were performed in animal models. In seven studies, statins were delivered systemically via oral, intraperitoneal, intraosseous, subcutaneous, and percutaneous routes. Among the 12 studies, where statins were delivered locally, statin-coated implants were used in seven studies, whereas in the remaining studies, statins were delivered via topical application on the bone cavities. The follow-up duration ranged between 1 and 12 weeks. Results from 18 studies showed that statin administration enhanced new bone formation (NBF) around implants and/or bone-to-implant contact. One study showed that statin-coated implant surfaces impaired osseointegration. Seven studies reported that statin administration enhanced NBF around implants in osteoporotic rats. On experimental grounds, local and systemic statin delivery seems to enhance osseointegration; however, from a clinical perspective, further studies are needed to assess the role of statins in promoting osseointegration around dental implants.

  3. Educational Data Mining Application for Estimating Students Performance in Weka Environment

    Science.gov (United States)

    Gowri, G. Shiyamala; Thulasiram, Ramasamy; Amit Baburao, Mahindra

    2017-11-01

    Educational data mining (EDM) is a multi-disciplinary research area that examines artificial intelligence, statistical modeling and data mining with the data generated from an educational institution. EDM utilizes computational ways to deal with explicate educational information keeping in mind the end goal to examine educational inquiries. To make a country stand unique among the other nations of the world, the education system has to undergo a major transition by redesigning its framework. The concealed patterns and data from various information repositories can be extracted by adopting the techniques of data mining. In order to summarize the performance of students with their credentials, we scrutinize the exploitation of data mining in the field of academics. Apriori algorithmic procedure is extensively applied to the database of students for a wider classification based on various categorizes. K-means procedure is applied to the same set of databases in order to accumulate them into a specific category. Apriori algorithm deals with mining the rules in order to extract patterns that are similar along with their associations in relation to various set of records. The records can be extracted from academic information repositories. The parameters used in this study gives more importance to psychological traits than academic features. The undesirable student conduct can be clearly witnessed if we make use of information mining frameworks. Thus, the algorithms efficiently prove to profile the students in any educational environment. The ultimate objective of the study is to suspect if a student is prone to violence or not.

  4. Efficacy, safety and tolerability of ongoing statin plus ezetimibe versus doubling the ongoing statin dose in hypercholesterolemic Taiwanese patients: an open-label, randomized clinical trial

    Directory of Open Access Journals (Sweden)

    Yu Chih-Chieh

    2012-05-01

    Full Text Available Abstract Background Reducing low-density lipoprotein cholesterol (LDL-C is associated with reduced risk for major coronary events. Despite statin efficacy, a considerable proportion of statin-treated hypercholesterolemic patients fail to reach therapeutic LDL-C targets as defined by guidelines. This study compared the efficacy of ezetimibe added to ongoing statins with doubling the dose of ongoing statin in a population of Taiwanese patients with hypercholesterolemia. Methods This was a randomized, open-label, parallel-group comparison study of ezetimibe 10 mg added to ongoing statin compared with doubling the dose of ongoing statin. Adult Taiwanese hypercholesterolemic patients not at optimal LDL-C levels with previous statin treatment were randomized (N = 83 to ongoing statin + ezetimibe (simvastatin, atorvastatin or pravastatin + ezetimibe at doses of 20/10, 10/10 or 20/10 mg or doubling the dose of ongoing statin (simvastatin 40 mg, atorvastatin 20 mg or pravastatin 40 mg for 8 weeks. Percent change in total cholesterol, LDL-C, high-density lipoprotein cholesterol (HDL-C and triglycerides, and specified safety parameters were assessed at 4 and 8 weeks. Results At 8 weeks, patients treated with statin + ezetimibe experienced significantly greater reductions compared with doubling the statin dose in LDL-C (26.2% vs 17.9%, p = 0.0026 and total cholesterol (20.8% vs 12.2%, p = 0.0003. Percentage of patients achieving treatment goal was greater for statin + ezetimibe (58.6% vs doubling statin (41.2%, but the difference was not statistically significant (p = 0.1675. The safety and tolerability profiles were similar between treatments. Conclusion Ezetimibe added to ongoing statin therapy resulted in significantly greater lipid-lowering compared with doubling the dose of statin in Taiwanese patients with hypercholesterolemia. Studies to assess clinical outcome benefit are ongoing. Trial registration Registered at ClinicalTrials.gov: NCT00652327

  5. The risk of cancer in users of statins

    NARCIS (Netherlands)

    Graaf, Matthijs R.; Beiderbeck, Annette B.; Egberts, Antoine C. G.; Richel, Dick J.; Guchelaar, Henk-Jan

    2004-01-01

    Purpose Several preclinical studies suggested a role for 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) in the treatment of cancer. The objective of this study was to compare the risk of incident cancer between users of statins and users of other cardiovascular medication.

  6. Sympathoinhibitory effect of statins in chronic heart failure.

    NARCIS (Netherlands)

    Gomes, M.E.R.; Lenders, J.W.M.; Bellersen, L.; Verheugt, F.W.A.; Smits, P.; Tack, C.J.J.

    2010-01-01

    OBJECTIVES: Increased (central) sympathetic activity is a key feature of heart failure and associated with worse prognosis. Animal studies suggest that statin therapy can reduce central sympathetic outflow. This study assessed statin effects on (central) sympathetic activity in human chronic heart

  7. Use of statins and the risk of rheumatoid arthritis.

    NARCIS (Netherlands)

    Jong, H. de; Klungel, O.; Dijk, L. van; Vandebriel, R.; Leufkens, H.; Cohen Tervaert, J.W.; Lovenren, H. van

    2009-01-01

    Background: Statins exert immunomodulatory effects which can cause immune-dysregulation and potentially lead to autoimmune reactions. Objectives: To study the association between use of statins and the risk of incident rheumatoid arthritis (RA). Methods: We conducted a case-control study using The

  8. Data Mining for 3D Organic Dirac Materials

    Science.gov (United States)

    Geilhufe, R. Matthias; Borysov, Stanislav S.; Bouhon, Adrien; Balatsky, Alexander V.

    The study of Dirac materials, i.e. materials where the low-energy fermionic excitations behave as massless Dirac particles has been of ongoing interest for more than two decades. Such massless Dirac fermions are characterized by a linear dispersion relation with respect to the particle momentum. A combined study using group theory and data mining within the Organic Materials Database leads to the discovery of stable Dirac-point nodes and Dirac line-nodes within the electronic band structure in the class of 3-dimensional organic crystals. The nodes are protected by crystalline symmetry. As a result of this study, we present band structure calculations and symmetry analysis for previously synthesized organic materials. In all these materials, the Dirac nodes are well separated within the energy and located near the Fermi surface, which opens up a possibility for their direct experimental observation. The authors acknowledge support by the US Department of Energy, BES E3B7, the swedish Research Council Grant No. 638-2013-9243, the Knut and Alice Wallenberg Foundation, and the European Research Council (FP/2207-2013)/ERC Grant Agreement No. DM-321031.

  9. Profiling Oman education data using data mining approach

    Science.gov (United States)

    Alawi, Sultan Juma Sultan; Shaharanee, Izwan Nizal Mohd; Jamil, Jastini Mohd

    2017-10-01

    Nowadays, with a large amount of data generated by many application services in different learning fields has led to the new challenges in education field. Education portal is an important system that leads to a better development of education field. This research paper presents an innovative data mining techniques to understand and summarizes the information of Oman's education data generated from the Ministry of Education Oman "Educational Portal". This research embarks into performing student profiling of the Oman student database. This study utilized the k-means clustering technique to determine the students' profiles. An amount of 42484-student records from Sultanate of Oman has been extracted for this study. The findings of this study show the practicality of clustering technique to investigating student's profiles. Allowing for a better understanding of student's behavior and their academic performance. Oman Education Portal contain a large amounts of user activity and interaction data. Analyses of this large data can be meaningful for educator to improve the student performance level and recognize students who needed additional attention.

  10. Antiatherosclerotic effects of long-term maximally intensive statin therapy after acute coronary syndrome: insights from Study of Coronary Atheroma by Intravascular Ultrasound: Effect of Rosuvastatin Versus Atorvastatin.

    Science.gov (United States)

    Puri, Rishi; Nissen, Steven E; Shao, Mingyuan; Ballantyne, Christie M; Barter, Philip J; Chapman, M John; Erbel, Raimund; Libby, Peter; Raichlen, Joel S; Uno, Kiyoko; Kataoka, Yu; Nicholls, Stephen J

    2014-11-01

    Patients with acute coronary syndromes (ACS) display diffuse coronary atheroma instability and heightened risk of early and late recurrent coronary events. We compared the long-term antiatherosclerotic efficacy of high-intensity statins in patients with ACS when compared with stable disease. Study of Coronary Atheroma by Intravascular Ultrasound: Effect of Rosuvastatin Versus Atorvastatin (SATURN) used serial intravascular ultrasound measures of coronary atheroma volume in patients treated with rosuvastatin 40 mg or atorvastatin 80 mg for 24 months. The overall effect of high-intensity statins on the change in coronary percent atheroma volume and major adverse cardiovascular events (death/nonfatal myocardial infarction/coronary revascularization) were evaluated in this post hoc analysis. When compared with non-ACS patients (n=678), patients with ACS (n=361) were younger, actively smoking, and have had a previous myocardial infarction (all P<0.001). At baseline, patients with ACS exhibited lower high-density lipoprotein cholesterol (43.5±11 versus 45.8±11 mg/dL; P=0.002), a higher apolipoprotein B: apolipoprotein A-1 ratio (0.90±0.24 versus 0.83±0.24; P<0.001) and greater percent atheroma volume (37.3±8.5% versus 35.9±8.1%; P=0.01) when compared with non-ACS patients. Despite similar achieved levels of lipid and inflammatory markers after high-intensity statin therapy, patients with ACS demonstrated greater percent atheroma volume regression than non-ACS patients (-1.46±0.14 versus -0.89±0.13; P=0.003). After propensity-weighted multivariable adjustment, baseline percent atheroma volume (P<0.001) and an ACS clinical presentation (P=0.02) independently associated with plaque regression. The 24-month major adverse cardiovascular events-free survival was similar between patients with ACS and non-ACS (90.6 versus 92.9%; P=0.25). Long-term high-intensity statin therapy caused greater plaque regression and comparable major adverse cardiovascular events rates in

  11. Quality of research results in agro-economy by data mining

    Directory of Open Access Journals (Sweden)

    Vukelić Gordana

    2015-01-01

    Full Text Available Data Mining (DM through data in agroeconomy is a scientific method that enables researchers not to go through set research scenarioes that are predetermined assumptions and hypotheses on the basis of insignificant atributes. On the contrary, by data mining detection of these atributes is made possible, in general, those hiden facts that enable setting a hypothesis. The DM method does this by an iterative way, including key atributes and factors and their influence on the quality of agro-resources. The research was conducted on a random sample, by analyzing the quality of eggs. The research subject is the posibility of classifying and predicting significant variablesatributes that determine the level of egg quality. The research starts from the use of Data Mining, as an area of machine studies, which significantly helps researchers in optimizing research. The applied methodology during research includes analyticalsintetic procedures and methods of Data Mining, with a special focus on using Supervised linear discrimination analysis and the Decision Tree. The results indicate significant posibilities of using DM as an additional analytical procedure in performing agroresearch and it can be concluded that it contributes to an improvement in effectiveness and validity of process in performing these researches.

  12. Comparative analysis of data mining techniques for business data

    Science.gov (United States)

    Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

    2014-12-01

    Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.

  13. Clinical diabetes research using data mining: a Canadian perspective.

    Science.gov (United States)

    Shah, Baiju R; Lipscombe, Lorraine L

    2015-06-01

    With the advent of the digitization of large amounts of information and the computer power capable of analyzing this volume of information, data mining is increasingly being applied to medical research. Datasets created for administration of the healthcare system provide a wealth of information from different healthcare sectors, and Canadian provinces' single-payer universal healthcare systems mean that data are more comprehensive and complete in this country than in many other jurisdictions. The increasing ability to also link clinical information, such as electronic medical records, laboratory test results and disease registries, has broadened the types of data available for analysis. Data-mining methods have been used in many different areas of diabetes clinical research, including classic epidemiology, effectiveness research, population health and health services research. Although methodologic challenges and privacy concerns remain important barriers to using these techniques, data mining remains a powerful tool for clinical research. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

  14. Data Mining and Data Fusion for Enhanced Decision Support

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Shiraj [ORNL; Ganguly, Auroop R [ORNL; Gupta, Amar [University of Arizona

    2008-01-01

    The process of Data Mining converts information to knowledge by utilizing tools from the disciplines of computational statistics, database technologies, machine learning, signal processing, nonlinear dynamics, process modeling, simulation, and allied disciplines. Data Mining allows business problems to be analyzed from diverse perspectives, including dimensionality reduction, correlation and co-occurrence, clustering and classification, regression and forecasting, anomaly detection, and change analysis. The predictive insights generated from Data Mining can be further utilized through real-time analysis and decision sciences, as well as through human-driven analysis based on management by exceptions or by objectives, to generate actionable knowledge. The tools that enable the transformation of raw data to actionable predictive insights are collectively referred as Decision Support tools. This chapter presents a new formalization of the decision process, leading to a new Decision Superiority model, partially motivated by the Joint Directors of Laboratories (JDL) Data Fusion Model. In addition, it examines the growing importance of Data Fusion concepts.

  15. The First International Conference on Soft Computing and Data Mining

    CERN Document Server

    Ghazali, Rozaida; Deris, Mustafa

    2014-01-01

    This book constitutes the refereed proceedings of the First International Conference on Soft Computing and Data Mining, SCDM 2014, held in Universiti Tun Hussein Onn Malaysia, in June 16th-18th, 2014. The 65 revised full papers presented in this book were carefully reviewed and selected from 145 submissions, and organized into two main topical sections; Data Mining and Soft Computing. The goal of this book is to provide both theoretical concepts and, especially, practical techniques on these exciting fields of soft computing and data mining, ready to be applied in real-world applications. The exchanges of views pertaining future research directions to be taken in this field and the resultant dissemination of the latest research findings makes this work of immense value to all those having an interest in the topics covered.    

  16. Successful reintroduction of statin therapy after statin-associated rhabdomyolysis.

    Science.gov (United States)

    Simons, Janet E; Holbrook, Anne M; Don-Wauchope, Andrew C

    2015-01-01

    The case report demonstrates the successful use of an alternative statin after a statin-related episode of rhabdomyolysis. Statin-associated rhabdomyolysis is a serious adverse event with a very low incidence and is considered the most severe of the muscle-related side effects of the statins. Rechallenge with statins is not a recommended practice after rhabdomyolysis. The patient experienced a myocardial infarct 1 y after the episode of rhabdomyolysis. He used alternative lipid-lowering therapy for 2 y. His low-density lipoprotein cholesterol was not meeting typical secondary prevention targets. An alternative statin was introduced and the patient has been followed for 4 years without recurrence of the rhabdomyolysis. This case suggests it may be time to reconsider the accepted practice of permanently avoiding statin therapy after rhabdomyolysis. Copyright © 2015 National Lipid Association. Published by Elsevier Inc. All rights reserved.

  17. Genetically Guided Statin Therapy

    Science.gov (United States)

    2017-03-01

    outcomes known to be prevented by statin therapy, we examined hospitalizations for three diagnoses: acute myocardial infarction (MI), stroke, and...0.84 Tobacco 0.051368 (736) 0.043552 (5418) 1.69E-05 Follow-Up (days) 1325.7711 1260.4477 əe-20 Product Strength (mg) 45.9049 43.6006 0.000923...cholesterol. However, the ultimate goal of statin therapy is to decrease incidence of CAD, acute myocardial infarction and perhaps stroke. However, there is a

  18. Prediction of Thyroid Disease Using Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Irina Ioniţă

    2016-08-01

    Full Text Available Recently, thyroid diseases are more and more spread worldwide. In Romania, for example, one of eight women suffer from hypothyroidism, hyperthyroidism or thyroid cancer. Various research studies estimate that about 30% of Romanians are diagnosed with endemic goiter. The factors that affect the thyroid function are: stress, infection, trauma, toxins, low-calorie diet, certain medication etc. It is very important to prevent such diseases rather than cure them, because the majority of treatments consist in long term medication or in chirurgical intervention. The current study refers to the thyroid disease classification in two of the most common thyroid dysfunctions (hyperthyroidism and hypothyroidism among the population. The authors analyzed and compared four classification models: Naive Bayes, Decision Tree, Multilayer Perceptron and Radial Basis Function Network. The results indicate a significant accuracy for all the classification models mentioned above, the best classification rate being that of the Decision Tree model. The data set used to build and to validate the classifier was provided by the UCI machine learning repository and by a website with Romanian data. The framework for building and testing the classification models was KNIME Analytics Platform and Weka, two data mining software.

  19. Combining complex networks and data mining: Why and how

    Science.gov (United States)

    Zanin, M.; Papo, D.; Sousa, P. A.; Menasalvas, E.; Nicchi, A.; Kubik, E.; Boccaletti, S.

    2016-05-01

    The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

  20. Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

    Science.gov (United States)

    Fallah, Mina; Niakan Kalhori, Sharareh R

    2017-10-01

    Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

  1. International Conference on Computational Intelligence in Data Mining

    CERN Document Server

    Mohapatra, Durga

    2017-01-01

    The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science. .

  2. Spatio-Temporal Data Mining for Location-Based Services

    DEFF Research Database (Denmark)

    Gidofalvi, Gyozo

    . The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...

  3. 4th International conference on Knowledge Discovery and Data Mining

    CERN Document Server

    Knowledge Discovery and Data Mining

    2012-01-01

    The volume includes a set of selected papers extended and revised from the 4th International conference on Knowledge Discovery and Data Mining, March 1-2, 2011, Macau, Chin.   This Volume is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of knowledge discovery and data mining and learning to disseminate their latest research results and exchange views on the future research directions of these fields. 108 high-quality papers are included in the volume.

  4. Data mining with SPSS modeler theory, exercises and solutions

    CERN Document Server

    Wendler, Tilo

    2016-01-01

    Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.

  5. Advances in machine learning and data mining for astronomy

    CERN Document Server

    Way, Michael J

    2012-01-01

    Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health

  6. Advanced Data Mining of Leukemia Cells Micro-Arrays

    OpenAIRE

    Richard S. Segall; Ryan M. Pierce

    2009-01-01

    This paper provides continuation and extensions of previous research by Segall and Pierce (2009a) that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM). As Segall and Pierce (2009a) and Segall and Pierce (2009b) the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is pro...

  7. A novel Neuro-fuzzy classification technique for data mining

    Directory of Open Access Journals (Sweden)

    Soumadip Ghosh

    2014-11-01

    Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.

  8. Data Mining for Expressivity of Recombinant Protein Expression

    Science.gov (United States)

    Kira, Satoshi; Isoai, Atsushi; Yamamura, Masayuki

    We analyzed the expressivity of recombinant proteins by using data mining methods. The expression technique of recombinant protein is a key step towards elucidating the functions of genes discovered through genomic sequence projects. We have studied the productive efficiency of recombinant proteins in fission yeast, Schizosaccharomyces pombe (S.pombe), by mining the expression results. We gathered 57 proteins whose expression levels were known roughly in the host. Correlation analysis, principal component analysis and decision tree analysis were applied to these expression data. Analysis featuring codon usage and amino acid composition clarified that the amino acid composition affected to the expression levels of a recombinant protein strongly than the effect of codon usage. Furthermore, analysis of amino acid composition showed that protein solubility and the metabolism cost of amino acids correlated with a protein expressivity. Codon usage was often interesting in the field of recombinant expressions. However, our analysis found the weak correlation codon features with expressivities. These results indicated that ready-made indices of codon bias were irrelevant ones for modeling the expressivities of recombinant proteins. Our data driven approach was an easy and powerful method to improve recombinant protein expression, and this approach should be concentrated attention with the huge amount of expression data accumulating through the post-genome era.

  9. Simulation of California's Major Reservoirs Outflow Using Data Mining Technique

    Science.gov (United States)

    Yang, T.; Gao, X.; Sorooshian, S.

    2014-12-01

    The reservoir's outflow is controlled by reservoir operators, which is different from the upstream inflow. The outflow is more important than the reservoir's inflow for the downstream water users. In order to simulate the complicated reservoir operation and extract the outflow decision making patterns for California's 12 major reservoirs, we build a data-driven, computer-based ("artificial intelligent") reservoir decision making tool, using decision regression and classification tree approach. This is a well-developed statistical and graphical modeling methodology in the field of data mining. A shuffled cross validation approach is also employed to extract the outflow decision making patterns and rules based on the selected decision variables (inflow amount, precipitation, timing, water type year etc.). To show the accuracy of the model, a verification study is carried out comparing the model-generated outflow decisions ("artificial intelligent" decisions) with that made by reservoir operators (human decisions). The simulation results show that the machine-generated outflow decisions are very similar to the real reservoir operators' decisions. This conclusion is based on statistical evaluations using the Nash-Sutcliffe test. The proposed model is able to detect the most influential variables and their weights when the reservoir operators make an outflow decision. While the proposed approach was firstly applied and tested on California's 12 major reservoirs, the method is universally adaptable to other reservoir systems.

  10. An Integrative data mining approach to identifying Adverse ...

    Science.gov (United States)

    The Adverse Outcome Pathway (AOP) framework is a tool for making biological connections and summarizing key information across different levels of biological organization to connect biological perturbations at the molecular level to adverse outcomes for an individual or population. Computational approaches to explore and determine these connections can accelerate the assembly of AOPs. By leveraging the wealth of publicly available data covering chemical effects on biological systems, computationally-predicted AOPs (cpAOPs) were assembled via data mining of high-throughput screening (HTS) in vitro data, in vivo data and other disease phenotype information. Frequent Itemset Mining (FIM) was used to find associations between the gene targets of ToxCast HTS assays and disease data from Comparative Toxicogenomics Database (CTD) by using the chemicals as the common aggregators between datasets. The method was also used to map gene expression data to disease data from CTD. A cpAOP network was defined by considering genes and diseases as nodes and FIM associations as edges. This network contained 18,283 gene to disease associations for the ToxCast data and 110,253 for CTD gene expression. Two case studies show the value of the cpAOP network by extracting subnetworks focused either on fatty liver disease or the Aryl Hydrocarbon Receptor (AHR). The subnetwork surrounding fatty liver disease included many genes known to play a role in this disease. When querying the cpAOP

  11. Application of Data Mining Algorithm to Recipient of Motorcycle Installment

    Directory of Open Access Journals (Sweden)

    Harry Dhika

    2015-12-01

    Full Text Available The study was conducted in the subsidiaries that provide services of finance related to the purchase of a motorcycle on credit. At the time of applying, consumers enter their personal data. Based on the personal data, it will be known whether the consumer credit data is approved or rejected. From 224 consumer data obtained, it is known that the number of consumers whose applications are approved is 87% or about 217 consumers and consumers whose application is rejected is 16% or as much as 6 consumers. Acceptance of motorcycle financing on credit by using the method of applying the algorithm through CRIS-P DM is the industry standard in the processing of data mining. The algorithm used in the decision making is the algorithm C4.5. The results obtained previously, the level of accuracy is measured with the Confusion Matrix and Receiver Operating characteristic (ROC. Evaluation of the Confusion Matrix is intended to seek the value of accuracy, precision value, and the value of recall data. While the Receiver Operating Characteristic (ROC is used to find data tables and comparison Area Under Curve (AUC.

  12. Automatic detection of interictal spikes using data mining models.

    Science.gov (United States)

    Valenti, Pablo; Cazamajou, Enrique; Scarpettini, Marcelo; Aizemberg, Ariel; Silva, Walter; Kochen, Silvia

    2006-01-15

    A prospective candidate for epilepsy surgery is studied both the ictal and interictal spikes (IS) to determine the localization of the epileptogenic zone. In this work, data mining (DM) classification techniques were utilized to build an automatic detection model. The selected DM algorithms are: Decision Trees (J 4.8), and Statistical Bayesian Classifier (naïve model). The main objective was the detection of IS, isolating them from the EEG's base activity. On the other hand, DM has an attractive advantage in such applications, in that the recognition of epileptic discharges does not need a clear definition of spike morphology. Furthermore, previously 'unseen' patterns could be recognized by the DM with proper 'training'. The results obtained showed that the efficacy of the selected DM algorithms is comparable to the current visual analysis used by the experts. Moreover, DM is faster than the time required for the visual analysis of the EEG. So this tool can assist the experts by facilitating the analysis of a patient's information, and reducing the time and effort required in the process.

  13. Predicting survival causes after out of hospital cardiac arrest using data mining method.

    Science.gov (United States)

    Le Duff, Franck; Muntean, Cristian; Cuggia, Marc; Mabo, Philippe

    2004-01-01

    The prognosis of life for patients with heart failure remains poor. By using data mining methods, the purpose of this study was to evaluate the most important criteria for predicting patient survival and to profile patients to estimate their survival chances together with the most appropriate technique for health care. Five hundred and thirty three patients who had suffered from cardiac arrest were included in the analysis. We performed classical statistical analysis and data mining analysis using mainly Bayesian networks. The mean age of the 533 patients was 63 (+/- 17) and the sample was composed of 390 (73 %) men and 143 (27 %) women. Cardiac arrest was observed at home for 411 (77 %) patients, in a public place for 62 (12 %) patients and on a public highway for 60 (11 %) patients. The belief network of the variables showed that the probability of remaining alive after heart failure is directly associated to five variables: age, sex, the initial cardiac rhythm, the origin of the heart failure and specialized resuscitation techniques employed. Data mining methods could help clinicians to predict the survival of patients and then adapt their practices accordingly. This work could be carried out for each medical procedure or medical problem and it would become possible to build a decision tree rapidly with the data of a service or a physician. The comparison between classic analysis and data mining analysis showed us the contribution of the data mining method for sorting variables and quickly conclude on the importance or the impact of the data and variables on the criterion of the study. The main limit of the method is knowledge acquisition and the necessity to gather sufficient data to produce a relevant model.

  14. The case for statin therapy in chronic heart failure

    NARCIS (Netherlands)

    van der Harst, Pim; Boehm, Michael; van Gilst, Wiek H.; van Veldhuisen, Dirk J.

    Both primary and secondary prevention studies have provided a wealth of evidence that statin therapy effectively reduces cardiovascular events. However, this general statement on the efficacy and safety of statin treatment has not been validated in patients with chronic heart failure (CHF).

  15. Considerations for supplementing with coenzyme Q10 during statin therapy.

    Science.gov (United States)

    Levy, Hedva Barenholtz; Kohlhaas, Heather K

    2006-02-01

    To review the literature concerning the effects of statin use on coenzyme (Co) Q10 concentrations and explain the rationale behind considering CoQ10 supplementation. A MEDLINE search was conducted through January 2006. Search terms included ubiquinone, coenzyme Q10, HMG-CoA reductase inhibitors, statins, myotoxicity, and clinical trials. Statin therapy reduces blood CoQ10 concentrations. Studies exploring how this affects the development of myotoxicity have been small and dissimilar, thus limiting the ability to draw strong conclusions. Isolated studies suggested that statins induce mitochondrial dysfunction, but the clinical implications of this effect are limited. Limited data suggest that patients with familial hypercholesterolemia, heart failure, or who are over 65 years of age might represent at-risk populations who would benefit from CoQ10 supplementation. Routine CoQ10 supplementation for all patients taking statins to prevent myotoxicity is not recommended. However, certain subpopulations might be at risk and warrant further study.

  16. Modeling and Data Mining in Blogosphere

    CERN Document Server

    Agarwal, Nitin

    2009-01-01

    This book offers a comprehensive overview of the various concepts and research issues about blogs or weblogs. It introduces techniques and approaches, tools and applications, and evaluation methodologies with examples and case studies. Blogs allow people to express their thoughts, voice their opinions, and share their experiences and ideas. Blogs also facilitate interactions among individuals creating a network with unique characteristics. Through the interactions individuals experience a sense of community. We elaborate on approaches that extract communities and cluster blogs based on informa

  17. Which patients may benefit from the use of a decision support system to improve compliance of physician decisions with clinical practice guidelines: a case study with breast cancer involving data mining.

    Science.gov (United States)

    Séroussi, Brigitte; Soulet, Arnaud; Spano, Jean-Philippe; Lefranc, Jean-Pierre; Cojean-Zelek, Isabelle; Blaszka-Jaulerry, Brigitte; Zelek, Laurent; Durieux, Axel; Tournigand, Christophe; Messai, Nizar; Rousseau, Alexandra; Bouaud, Jacques

    2013-01-01

    OncoDoc2 is a guideline-based clinical decision support system (CDSS) for breast cancer management. It has been used as an intervention in a randomized controlled trial carried out to evaluate the impact of using a CDSS upon the compliance with clinical practice guidelines (CPGs) of multidisciplinary staff meeting decisions. Data mining was used to discover multi-criteria regularities as "emerging patterns" (EPs) associated with compliance and non-compliance with CPGs when using and not using OncoDoc2 and to assess which patients may benefit from the use of the CDSS. Decision data was collected from all participating centers. The number of EPs associated with non-compliance is smaller in the intervention arm, which suggests a practice harmonization effect of OncoDoc2. EPs associated with compliant decisions in both arms of the trial correspond to situations well identified in CPGs. EPs associated with non-compliant decisions when the system is not used are associated with compliance when the system is used except in clinical situations where evidence is lacking.

  18. Data mining and the human genome

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, Henry [The MITRE Corporation, McLean, VA (US). JASON Program Office; Callan, Curtis [The MITRE Corporation, McLean, VA (US). JASON Program Office; Dally, William [The MITRE Corporation, McLean, VA (US). JASON Program Office; Dyson, Freeman [The MITRE Corporation, McLean, VA (US). JASON Program Office; Hwa, Terence [The MITRE Corporation, McLean, VA (US). JASON Program Office; Koonin, Steven [The MITRE Corporation, McLean, VA (US). JASON Program Office; Levine, Herbert [The MITRE Corporation, McLean, VA (US). JASON Program Office; Rothaus, Oscar [The MITRE Corporation, McLean, VA (US). JASON Program Office; Schwitters, Roy [The MITRE Corporation, McLean, VA (US). JASON Program Office; Stubbs, Christopher [The MITRE Corporation, McLean, VA (US). JASON Program Office; Weinberger, Peter [The MITRE Corporation, McLean, VA (US). JASON Program Office

    2000-01-07

    As genomics research moves from an era of data acquisition to one of both acquisition and interpretation, new methods are required for organizing and prioritizing the data. These methods would allow an initial level of data analysis to be carried out before committing resources to a particular genetic locus. This JASON study sought to delineate the main problems that must be faced in bioinformatics and to identify information technologies that can help to overcome those problems. While the current influx of data greatly exceeds what biologists have experienced in the past, other scientific disciplines and the commercial sector have been handling much larger datasets for many years. Powerful datamining techniques have been developed in other fields that, with appropriate modification, could be applied to the biological sciences.

  19. Interactive evolutionary algorithms and data mining for drug design

    NARCIS (Netherlands)

    Lameijer, Eric Marcel Wubbo

    2010-01-01

    One of the main problems of drug design is that it is quite hard to discover compounds that have all the required properties to become a drug (efficacy against the disease, good biological availability, low toxicity). This thesis describes the use of data mining and interactive evolutionary

  20. Data mining algorithms for land cover change detection: a review

    Indian Academy of Sciences (India)

    Sangram Panigrahi

    2017-11-24

    Nov 24, 2017 ... Abstract. Land cover change detection has been a topic of active research in the remote sensing community. Due to enormous amount of data available from satellites, it has attracted the attention of data mining researchers to search a new direction for solution. The Terra Moderate Resolution Imaging ...