Int. J. Comput. Model. Algorithms Medicine最新文献

{"title":"Prevalence of Metabolic Syndrome in Subjects with Osteoarthritis Stratified by Age and Sex: A Cross Sectional Analysis in NHANES III","authors":"A. Joshi","doi":"10.4018/jcmam.2010072005","DOIUrl":"https://doi.org/10.4018/jcmam.2010072005","url":null,"abstract":"","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125712875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification Systems for Bacterial Protein-Protein Interaction Document Retrieval","authors":"Hongfang Liu, Manabu Torii, Guixian Xu, Johannes Goll","doi":"10.4018/jcmam.2010072003","DOIUrl":"https://doi.org/10.4018/jcmam.2010072003","url":null,"abstract":"Protein-protein interaction (PPI) networks are essential to understand the fundamental processes governing cell biology. Recently, studying PPI networks becomes possible due to advances in experimental high-throughput genomics and proteomics technologies. Many interactions from such high-throughput studies and most interactions from small-scale studies are reported only in the scientific literature and thus are not accessible in a readily analyzable format. This has led to the birth of manual curation initiatives such as the International Molecular Exchange Consortium (IMEx). The manual curation of PPI knowledge can be accelerated by text mining systems to retrieve PPI-relevant articles (article retrieval) and extract PPI-relevant knowledge (information extraction). In this article, the authors focus on article retrieval and define the task as binary classification where PPI-relevant articles are positives and the others are negatives. In order to build such classifier, an annotated corpus is needed. It is very expensive to obtain an annotated corpus manually but a noisy and imbalanced annotated corpus can be obtained automatically, where a collection of positive documents can be retrieved from existing PPI knowledge bases and a large number of unlabeled documents (most of them are negatives) can be retrieved from PubMed. They compared the performance of several machine learning algorithms by varying the ratio of the number of positives to the number of unlabeled documents and the number of features used. DOI: 10.4018/jcmam.2010072003 IGI PUBLISHING This paper appears in the publication, International Journal of Computational Models and Algorithms in Medicine, Volume 1, Issue 1 edited by Aryya Gangopadhyay © 2010, IGI Global 701 E. Choc late Avenue, Hersh y PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-global.com ITJ 5528 International Journal of Computational Models and Algorithms in Medicine, 1(1), 34-44, January-March 2010 35 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. ized way and to avoid duplication of efforts, IMEx1 databases such as IntAct (http://www. ebi.ac.uk/intact), DIP (Database of Interacting Proteins; http://dip.doe-mbi.ucla.edu), MINT (Molecular Interactions Database; http://mint. bio.uniroma2.it/mint) and MPIDB (Microbial Protein Interaction Database; http://www. jcvi.org/mpidb) conduct coordinated manual literature curation. Text mining system to prioritize articles for curators according to their PPI relevance can accelerate such curation processes significantly. For example, MPIDB curators scan a whole issue (20 to 50 articles) of the Journal of Bacteriology or Molecular Microbiology and find approximately 10% of these articles report interaction experiments. Thus, the curators spend roughly 90% of their time reading irrelevant articles. A text mining system to prioritize articles for curators can be dev","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"71 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126133771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Formal Approach to Evaluating Medical Ontology Systems using Naturalness","authors":"Y. J. An, Kuo-Chuan Huang, Soon Ae Chun, J. Geller","doi":"10.4018/jcmam.2010072001","DOIUrl":"https://doi.org/10.4018/jcmam.2010072001","url":null,"abstract":"Ontologies, terminologies and vocabularies are popular repositories for collecting the terms used in a domain. It may be expected that in the future more such ontologies will be created for domain experts. However, there is increasing interest in making the language of experts understandable to casual users. For example, cancer patients often research their cases on the Web. The authors consider the problem of objectively evaluating the quality of ontologies (QoO). This article formalizes the notion of naturalness as a component of QoO and quantitatively measures naturalness for well-known ontologies (UMLS, WordNet, OpenCyc) based on their concepts, IS-A relationships and semantic relationships. To compute numeric values characterizing the naturalness of an ontology, this article defines appropriate metrics. As absolute numbers in such a pursuit are often meaningless, we concentrate on using relative naturalness metrics. That allows us to say that a certain ontology is relatively more natural than another one. DOI: 10.4018/jcmam.2010072001 IGI PUBLISHING This paper appears in the publication, International Journal of Computational Models and Algorithms in Medicine, Volume 1, Issue 1 edited by Aryya Gangopadhyay © 2010, IGI Global 701 E. Chocolate Avenue, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-global.com ITJ 5526 2 International Journal of Computational Models and Algorithms in Medicine, 1(1), 1-18, January-March 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. factory result, but the user might be satisfied with the search result for the more general term Penicillin. Finding broader or narrower concepts of a given concept is an important technique, which is recommended as a Web search strategy. According to Kalfoglou & Hu (2006), application ontologies are converging with the Web. Thus the knowledge provided by ontologies should be filtered dynamically by understanding the needs of Web users. There are several well-known ontologies, which many researchers have used and referenced, such as UMLS, WordNet and OpenCyc. Some researchers have presented modified or enriched ontological models by adding new types and trimming some detailed relationships from existing ontologies (Stone et al., 2004). On the other hand, research that investigates these ontologies not only from the view point of experts but also from the perspective of casual users is rare. Assessing difficulties in understanding and using ontologies for emerging user communities on the Semantic Web should be conducted as a stage of implementing the Semantic Web (Finin et al., 2007). In his original work on ontologies, Gruber (1993) stressed that ontologies are about knowledge sharing. We raise the question whether existing ontologies are constructed so that they may succeed at knowledge sharing. Zeng et al. (2005) showed that communication through termi","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128598022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rational Drug Design: One Target, Many Paths to It","authors":"K. Barakat, M. Houghton, D. Tyrrel, J. Tuszynski","doi":"10.4018/ijcmam.2014010104","DOIUrl":"https://doi.org/10.4018/ijcmam.2014010104","url":null,"abstract":"For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target’s 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used. Rational Drug Design: One Target, Many Paths to It","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Case Study to Assess Implementation of Electronic Health Records at Central Flacq Hospital, Mauritius","authors":"M. Arora, A. Joshi","doi":"10.4018/jcmam.2012010101","DOIUrl":"https://doi.org/10.4018/jcmam.2012010101","url":null,"abstract":"The challenges of implementing electronic health records (EHRs) have received some attention, but little is known about the process of transitioning from paper-based to electronic files. In this paper, a mixed approach using combined qualitative and quantitative methods is utilized. The authors enrolled nine administrative managers and 87 employees from different backgrounds, all part of a regional hospital at Flacq in Mauritius, from April to May 2011. Employees responded to a survey on various aspects pertaining to their eagerness to accept the shift to electronic health records and their views on the probability of success. Descriptive and inferential statistics were used to analyze the quantitative results and content analysis was performed on the qualitative data. Nurses performing at middle level agreed that a shift to EHR is positive but felt that it might take a long time to effect the change. With its implementation, they agreed that advantages like up to date information, diminished workload, and cost effectiveness would be easily attained. In contrast, focus groups confirmed that without the collaboration and support of management, implementation of EHR would prove arduous. DOI: 10.4018/jcmam.2012010101 2 International Journal of Computational Models and Algorithms in Medicine, 3(1), 1-13, January-March 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. In the wake of an e-health strategy for Mauritius, challenges in implementing electronic health records (EHRs) have received some attention, but little is known about the process of transitioning from paper-based files to electronic filing. Implementing an EMR is probably the most difficult, significant, and potentially beneficial change a practice can make. The change has wide-ranging impact on the experience of everyone in the office, from physicians to staff, and to patients. When done correctly it can yield benefits on the quality of patient care, ease of charting, and improvement in revenue. When done wrong, it creates longer working hours, decreased revenue, employee dissatisfaction with work, and encroachment upon the personal time. Making any change to a large extent is never easy, and most physicians describe starting an EMR in their practice as one of the most difficult organizational experiences that their practice had to face.","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130206862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Multidimensional Real-Time Data Analysis: A Case Study for the Detection of Apnoea of Prematurity","authors":"C. Catley","doi":"10.4018/jcmam.2011010102","DOIUrl":"https://doi.org/10.4018/jcmam.2011010102","url":null,"abstract":"In this paper, the authors present a framework to support multidimensional analysis of real-time physiological data streams and clinical data. The clinical context for the case study demonstration is neonatal intensive care, focusing specifically on the detection of episodes of central apnoea, a clinically significant problem. The model accounts for the multidimensional and real-time nature of apnoea of prematurity and the associated clinical rules. The framework demonstration includes: 1) defining rules that quantify concurrent behaviours between multiple synchronous data streams and asynchronous data values; 2) designing UML models to define present practice event processing for episodes of apnoea; 3) translating the model in SPADE to enable the deployment within the real-time processing layer of the Artemis platform, which utilizes IBM’s InfoSphere Streams; 4) demonstrating knowledge discovery with simple and complex temporal abstractions of the data streams; and 5) presenting results for early detection of episodes of apnoea across multiple physiological data streams. dimensional data where multiple elements, each representing a dimension that can vary in value, characterize an item of interest. Real-time monitoring of multiple physiological time series data streams and the interactions between these streams, as well as including clinical informaDOI: 10.4018/jcmam.2011010102 International Journal of Computational Models and Algorithms in Medicine, 2(1), 16-37, January-March 2011 17 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. tion from diverse patient information systems, represents an important but non-trivial problem. We have developed a framework to support multidimensional real-time data analysis of both physiological time series data streams and clinical data, such as that obtained from hospital Clinical Information Management Systems (CIMS) or laboratory information systems. Such a framework is relevant to clinical environments associated with real-time monitoring, such as intensive care and post-operative care, obstetrics, and cardiology. Drawing on the authors’ experience, we demonstrate the framework within the context of neonatal intensive care, presenting a case study for the detection and classification of episodes of apnoea, based on analyzing the interactions between multiple physiological signals in association with other relevant clinical information. Patient support in the Neonatal Intensive Care Unit (NICU) includes obtaining realtime physiological data from bedside medical devices to support the diagnosis and treatment of critically ill newborn infants. NICU clinical monitoring systems must be capable of handling multidimensional data; examples of varying dimensions include the number of data streams collected, the length and behaviour of the streams, and the number of events present or to be detected in the streams. While current ","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115109847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Methodology for Clustering Transient Biomedical Signals by Variable","authors":"Pimwadee Chaovalit","doi":"10.4018/jcmam.2012010103","DOIUrl":"https://doi.org/10.4018/jcmam.2012010103","url":null,"abstract":"Biomedical signals which help monitor patients’ physical conditions are a crucial part of the healthcare industry. The healthcare professionals’ ability to monitor patients and detect early signs of conditions such as blocked arteries and abnormal heart rhythms can be accomplished by performing data clustering on biomedical signals. More importantly, clustering on streams of biomedical signals make it possible to look for patterns that may indicate developing conditions. While there are a number of clustering algorithms that perform data streams clustering by example, few algorithms exist that perform clustering by variable. This paper presents POD-Clus, a clustering method which uses a model-based clustering principle and, in addition to clustering by example, also cluster data streams by variable. The clustering result from POD-Clus was superior to the result from ODAC, a baseline algorithm, for both with and without cluster evolutions. DOI: 10.4018/jcmam.2012010103 International Journal of Computational Models and Algorithms in Medicine, 3(1), 32-71, January-March 2012 33 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. leading to a total of 1.2 million data points. This is an enormous amount of data collected within a short period of time. As various heart conditions can be revealed from the analysis of heartbeats. For example, a heart-related condition such as blocked blood supply will generate tissue death and reflect in the abnormal height of heartbeat waves. Therefore, analyzing these large amounts of data in a timely manner for a quick diagnosis is challenging, as the data may become too large to either deliver over a network (Sun & Sclabassi, 1999) or store in the main memory of the device. For this reason, data streams processing needs to happen real-time while data streams arrive. As various types of biomedical signals can be considered data streams, there exists a need for effective data streams mining techniques that can handle such data streams efficiently. Data streams’ characteristics (Domingos & Hulten, 2000; Gama, Rodrigues, & Aguilar-Ruiz, 2007) can be described as follows: • Data from the streams usually come in at a detailed level, e.g., 1000 Hz. • Streaming data arrives at a fast pace, therefore agile data management and utilization is key. • Observations of data are potentially unbounded. • Storage and memory resources for processing data streams are possibly limited. Data streams clustering can be incorporated into a computer-aided analysis used by physicians to cluster biomedical signals for diagnosis on the patients. By grouping biomedical signals into homogeneous clusters, we learn about data characteristics which may indicate developing conditions. Results from clustering can then be developed into classification models or predictive models useful in healthcare diagnoses. As Chaovalit (2010) has proposed the POD-Clus algorithm (P","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131904616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mobile Agent-Based Technique for Medical Monitoring (Supports of Patients with Diabetes)","authors":"Z. Chaouch, Mohammed Tamali","doi":"10.4018/ijcmam.2014010102","DOIUrl":"https://doi.org/10.4018/ijcmam.2014010102","url":null,"abstract":"Telemedicine is a particularly useful means to optimize the quality of care by fast medical exchanges that benefit patients whose state of health requires an appropriate and fast response, regardless of their geographic location. In this paper, the authors propose a mobile agent based architecture (DiabMAS) for remote medical monitoring of diabetic patients on an outpatient basis using mobile devices (laptops, PDAs, etc ...) by exploring the new operating Mobile system, Android. DiabMAS is a multi-agent system having as main objective the improvement of the transmission of information between patients and their physicians, especially the management of specific and critical cases. A Mobile Agent-Based Technique for Medical Monitoring (Supports of Patients with Diabetes)","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126444881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Partial Optimization Approach for Privacy Preserving Frequent Itemset Mining","authors":"Shibnath Mukherjee, A. Gangopadhyay, Zhiyuan Chen","doi":"10.4018/jcmam.2010072002","DOIUrl":"https://doi.org/10.4018/jcmam.2010072002","url":null,"abstract":"While data mining has been widely acclaimed as a technology that can bring potential benefits to organizations, such efforts may be negatively impacted by the possibility of discovering sensitive patterns, particularly in patient data. In this article the authors present an approach to identify the optimal set of transactions that, if sanitized, would result in hiding sensitive patterns while reducing the accidental hiding of legitimate patterns and the damage done to the database as much as possible. Their methodology allows the user to adjust their preference on the weights assigned to benefits in terms of the number of restrictive patterns hidden, cost in terms of the number of legitimate patterns hidden, and damage to the database in terms of the difference between marginal frequencies of items for the original and sanitized databases. Most approaches in solving the given problem found in literature are all-heuristic based without formal treatment for optimality. While in a few work, ILP has been used previously as a formal optimization approach, the novelty of this method is the extremely low cost-complexity model in contrast to the others. They implement our methodology in C and C++ and ran several experiments with synthetic data generated with the IBM synthetic data generator. The experiments show excellent results when compared to those in the literature. DOI: 10.4018/jcmam.2010072002 IGI PUBLISHING This paper appears in the publication, International Journal of Computational Models and Algorithms in Medicine, Volume 1, Issue 1 edited by Aryya Gangopadhyay © 2010, IGI Global 701 E. Chocolate Avenue, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-global.com ITJ 5527 20 International Journal of Computational Models and Algorithms in Medicine, 1(1), 19-33, January-March 2010 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 2002; Oliviera et al., 2003a, 2003b; Han et al., 2006). A number of cases have been reported in literature where data mining actually has posed threats to discovery of sensitive knowledge and violating privacy. One typical problem is that of inferencing, which means inferring sensitive information from non-sensitive or unclassified data (Oliviera et al., 2002; Clifton, 2001). Data mining is part of the larger business intelligence initiatives that are taking place in organizations across government and industry sectors, many of which include medical applications. It is being used for prediction as well knowledge discovery that can lead to cost reduction, business expansion, and detection of fraud or wastage of resources, among other things. With its many benefits, data mining has given rise to increasingly complex and controversial privacy issues. For example, the privacy implications of data mining have lead to high profile controversies involving the use of data mining tools and techniques on data","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128796363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subspace Discovery for Disease Management: A Case Study in Metabolic Syndrome","authors":"J. Namayanja, V. Janeja","doi":"10.4018/jcmam.2011010103","DOIUrl":"https://doi.org/10.4018/jcmam.2011010103","url":null,"abstract":"This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics. subsets in the data. Essentially we focus on using data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. We focus on a case study in Metabolic Syndrome (MetS). MetS can be described as a combination of abnormalities that occur in the body during the processing of food and nutrients (Wright, 2005). A number of definitions have been studied to classify MetS; however, there is no clear criterion that can generally fit into a single satisfactory protocol. This is primarily because this domain encompasses quite a DOI: 10.4018/jcmam.2011010103 International Journal of Computational Models and Algorithms in Medicine, 2(1), 38-59, January-March 2011 39 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. variety of demographics in society leading to an implication that different criteria may be appropriate for different demographic strata. Our research addresses this issue and identifies the cross section of demographic strata and the disease characteristics which are critical for understanding the disease in that subset of the population. We begin by first outlining the motivation of the case study by discussing the challenges in studying Metabolic Syndrome in general and then outlining the data mining challenges.","PeriodicalId":162417,"journal":{"name":"Int. J. Comput. Model. Algorithms Medicine","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130401883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}