Health Informatics Data Analysis Methods and Examples /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Xu, Dong (Επιμελητής έκδοσης), Wang, May D. (Επιμελητής έκδοσης), Zhou, Fengfeng (Επιμελητής έκδοσης), Cai, Yunpeng (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Health Information Science,
• Θέματα: Computer science.; Health informatics.; Data mining.; Bioinformatics.; Biomathematics.; Computer Science.; Health Informatics.; Data Mining and Knowledge Discovery.; Computational Biology/Bioinformatics.; Genetics and Population Dynamics.

 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. 

 1 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.