TY - BOOK ID - 16191542 TI - Health Informatics Data Analysis : Methods and Examples AU - Xu, Dong. AU - Wang, May D. AU - Zhou, Fengfeng. AU - Cai, Yunpeng. PY - 2017 SN - 3319449818 3319449796 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer science. KW - Health informatics. KW - Data mining. KW - Bioinformatics. KW - Biomathematics. KW - Computer Science. KW - Health Informatics. KW - Data Mining and Knowledge Discovery. KW - Computational Biology/Bioinformatics. KW - Genetics and Population Dynamics. KW - Medicine KW - Data processing. KW - Computers in medicine KW - Medical records KW - Genetics KW - Mathematics. KW - Biology KW - Embryology KW - Mendel's law KW - Adaptation (Biology) KW - Breeding KW - Chromosomes KW - Heredity KW - Mutation (Biology) KW - Variation (Biology) KW - Bio-informatics KW - Biological informatics KW - Information science KW - Computational biology KW - Systems biology KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - EHR systems KW - EHR technology KW - EHRs (Electronic health records) KW - Electronic health records KW - Electronic medical records KW - EMR systems KW - EMRs (Electronic medical records) KW - Information storage and retrieval systems KW - Data processing KW - Medical care KW - Health Workforce KW - Mathematics KW - Clinical informatics KW - Health informatics KW - Medical information science UR - https://www.unicat.be/uniCat?func=search&query=sysid:16191542 AB - This 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. ER -