LEADER 04892nam 22006855 450 001 9910254843803321 005 20200705150456.0 010 $a3-319-44981-8 024 7 $a10.1007/978-3-319-44981-4 035 $a(CKB)4100000000587288 035 $a(DE-He213)978-3-319-44981-4 035 $a(MiAaPQ)EBC5043122 035 $a(PPN)204535441 035 $a(EXLCZ)994100000000587288 100 $a20170909d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHealth Informatics Data Analysis $eMethods and Examples /$fedited by Dong Xu, May D. Wang, Fengfeng Zhou, Yunpeng Cai 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (X, 210 p. 54 illus.) 225 1 $aHealth Information Science,$x2366-0988 311 $a3-319-44979-6 320 $aIncludes bibliographical references at the end of each chapters. 327 $a1 Electrocardiogram -- 2 EEG visualization and analysis techniques -- 3 Big health data mining -- 4 Computational infrastructure for tele-health -- 5 Identification and Functional Annotation of lncRNAs in human disease -- 6 Metabolomics characterization of human diseases -- 7 Metagenomics for Monitoring Environmental Biodiversity: Challenges, Progress, and Opportunities -- 8 Global nonlinearfitness function for protein structures -- 9 Clinical Assessment of Disease Risk Factors Using SNP Data and Bayesian Methods -- 10 Imaging genetics: information fusion and association techniques between biomedical images and genetic factors. 330 $aThis book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries. 410 0$aHealth Information Science,$x2366-0988 606 $aHealth informatics 606 $aData mining 606 $aBioinformatics 606 $aBiomathematics 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/H28009 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aGenetics and Population Dynamics$3https://scigraph.springernature.com/ontologies/product-market-codes/M31010 615 0$aHealth informatics. 615 0$aData mining. 615 0$aBioinformatics. 615 0$aBiomathematics. 615 14$aHealth Informatics. 615 24$aHealth Informatics. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputational Biology/Bioinformatics. 615 24$aGenetics and Population Dynamics. 676 $a610.285 702 $aXu$b Dong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b May D$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhou$b Fengfeng$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCai$b Yunpeng$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254843803321 996 $aHealth Informatics Data Analysis$92500582 997 $aUNINA