Healthcare Data Analytics

At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available to solve healthcare problems.The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients.Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: Healthcare Data Sources and Basic Analytics -details the various healthcare data sources and analytical techniques used in the processing and analysis of such data Advanced Data Analytics for Healthcare- covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics Applications and Practical Systems for Healthcare -covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision support Computer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain.

An Introduction to Healthcare Data Analytics; Chandan K. Reddy and Charu C. AggarwalIntroductionHealthcare Data Sources and Basic AnalyticsAdvanced Data Analytics for HealthcareApplications and Practical Systems for HealthcareResources for Healthcare Data AnalyticsConclusionsHEALTHCARE DATA SOURCES AND BASIC ANALYTICSElectronic Health Records: A Survey; Rajiur Rahman and Chandan K. ReddyIntroductionHistory of EHRComponents of HERCoding SystemsBenefits of EHRBarriers to Adopting EHRChallenges of Using EHR DataPhenotyping AlgorithmsConclusionsBiomedical Image Analysis; Dirk Padfield, Paulo Mendonca, and Sandeep GuptaIntroductionBiomedical Imaging ModalitiesObject DetectionImage SegmentationImage RegistrationFeature ExtractionConclusion and Future WorkMining of Sensor Data in Healthcare: A Survey; Daby Sow, Kiran K. Turaga, Deepak S. Turaga, and Michael SchmidtIntroductionMining Sensor Data in Medical Informatics: Scope and ChallengesChallenges in Healthcare Data AnalysisSensor Data Mining ApplicationsNonclinical Healthcare ApplicationsSummary and Concluding RemarksBiomedical Signal Analysis; Abhijit Patil, Rajesh Langoju, Suresh Joel, Bhushan D. Patil, and Sahika GencIntroductionTypes of Biomedical SignalsECG Signal Analysis.Denoising of SignalsMultivariate Biomedical Signal AnalysisCross-Correlation AnalysisRecent Trends in Biomedical Signal AnalysisDiscussionsGenomic Data Analysis for Personalized Medicine; Juan CuiIntroductionGenomic Data GenerationMethods and Standards for Genomic Data AnalysisTypes of Computational Genomics Studies towards Personalized MedicineGenetic and Genomic Studies to theBedside of Personalized MedicineConcluding RemarksNatural Language Processing and Data Mining for Clinical Text; Kalpana Raja and Siddhartha R. JonnalagaddaIntroductionNatural Language ProcessingMining Information from Clinical TextChallenges of Processing Clinical ReportsClinical ApplicationsConclusionsMining the Biomedical Literature; Claudiu Mihaila, Riza Batista-Navarro, Noha Alnazzawi, Georgios Kontonatsios, Ioannis Korkontzelos, Rafal Rak, Paul Thompson, and Sophia AnaniadouIntroductionResourcesTerminology Acquisition and ManagementInformationExtractionDiscourse InterpretationText Mining EnvironmentsApplicationsIntegration with Clinical Text MiningConclusionsSocial Media Analytics for Healthcare; Alexander KotovIntroductionSocial Media Analysis for Detection and Tracking of Infectious DiseaseSocial Media Analysis for Public Health ResearchAnalysis of Social Media Use in HealthcareConclusions and Future Directions ADVANCED DATA ANALYTICS FOR HEALTHCAREA Review of Clinical Prediction Models; Chandan K. Reddy and Yan LiIntroduction Basic Statistical Prediction ModelsAlternative Clinical Prediction ModelsSurvival ModelsEvaluation and ValidationConclusionTemporal Data Mining for Healthcare Data; Iyad BatalIntroductionAssociation AnalysisTemporal Pattern MiningSensor Data AnalysisOther Temporal Modeling MethodsResourcesSummaryVisual Analytics for Healthcare; David Gotz, Jesus Caban, and Annie T. ChenIntroductionIntroduction to Visual Analytics and Medical Data VisualizationVisual Analytics in HealthcareConclusionPredictive Models for Integrating Clinical and Genomic Data; Sanjoy Dey, Rohit Gupta, Michael Steinbach, and Vipin KumarIntroductionIssues and Challenges in Integrating Clinical and Genomic DataDifferent Types of IntegrationDifferent Goals of Integrative StudiesValidationDiscussion and Future WorkInformation Retrieval for Healthcare; William R. HershIntroductionKnowledge-Based Information in Healthcare and BiomedicineContent of Knowledge-Based Information ResourcesIndexingRetrievalEvaluationResearch DirectionsConclusionPrivacy-Preserving Data Publishing Methods in Healthcare; Yubin Park and Joydeep GhoshIntroductionData Overview and PreprocessingPrivacy-Preserving Publishing MethodsChallenges with Health DataConclusionAPPLICATIONS AND PRACTICAL SYSTEMS FOR HEALTHCARE Data Analytics for Pervasive Health; Giovanni Acampora, Diane J. Cook, Parisa Rashidi, and Athanasios V. VasilakosIntroductionSupporting Infrastructure and TechnologyBasic Analytic TechniquesAdvanced Analytic TechniquesApplicationsConclusions and Future OutlookFraud Detection in Healthcare; Varun Chandola, Jack Schryver, and Sreenivas SukumarIntroductionUnderstanding Fraud in the Healthcare SystemDefinition and Types of Healthcare FraudIdentifying Healthcare Fraud from DataKnowledge Discovery-Based Solutions for Identifying FraudConclusionsData Analytics for Pharmaceutical Discoveries; Shobeir Fakhraei, Eberechukwu Onukwugha, and Lise GetoorIntroductionChemical andBiologicalDataSpontaneous Reporting Systems (SRSs)Electronic Health RecordsPatient-Generated Data on the InternetBiomedical LiteratureSummary and Future ChallengesClinical Decision Support Systems; Martin Alther and Chandan K. ReddyIntroductionHistorical PerspectiveVarious Types of CDSSDecision Support during Care Provider Order EntryDiagnostic Decision SupportHuman-Intensive TechniquesChallenges of CDSSLegal and Ethical IssuesConclusionComputer-Assisted Medical Image Analysis Systems; Shu Liao, Shipeng Yu, Matthias Wolf, Gerardo Hermosillo, Yiqiang Zhan, Yoshihisa Shinagawa, Zhigang Peng, Xiang Sean Zhou, Luca Bogoni, and Marcos SalganicoffIntroductionComputer-Aided Diagnosis/Detection of DiseasesMedical Imaging Case StudiesConclusionsMobile Imaging and Analytics for Biomedical Data; Stephan M. Jonas and Thomas M. DesernoIntroductionImage FormationData VisualizationImage AnalysisImage Management and CommunicationIndex

Chandan K. Reddy is an associate professor in the Department of Computer Science at Wayne State University. He received his PhD from Cornell University and MS from Michigan State University. His primary research interests are in the areas of data mining and machine learning with applications to healthcare, bioinformatics, and social network analysis. His research is funded by the National Science Foundation, the National Institutes of Health, Department of Transportation, and the Susan G. Komen for the Cure Foundation. He has published over 50 peer-reviewed articles in leading conferences and journals. He received the Best Application Paper Award at the ACM SIGKDD conference in 2010 and was a finalist of the INFORMS Franz Edelman Award Competition in 2011. He is a senior member of IEEE and a life member of ACM. Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his BS from IIT Kanpur in 1993 and his PhD from the Massachusetts Institute of Technology in 1996. He has published more than 250 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is an author or editor of 13 books, including the first comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bioterrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, a recipient of the IBM Outstanding Technical Achievement Award (2009) for his work on data streams, and a recipient of an IBM Research Division Award (2008) for his contributions to System S. He also received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He has served as conference chair and associate editor at many reputed conferences and journals in data mining, general co-chair of the IEEE Big Data Conference (2014), and is editor-in-chief of the ACM SIGKDD Explorations. He is a fellow of the ACM and the IEEE, for "contributions to knowledge discovery and data mining algorithms."