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Medical Informatics

Health, Medicine & Nursing

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Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research.Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.

Methods in Biomedical Informatics

POST SECONDARY FOUR YEAR

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METHODS IN BIOMEDICAL INFORMATICS

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Contents.
Contributors.
1: Introduction.
2: Data Integration: An Overview.
3: Knowledge Representation.
4: Hypothesis Generation from Heterogeneous Datasets.
5: Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis.
6: Biomedical Natural Language Processing and Text Mining.
7: Knowledge Discovery in Biomedical Data: Theory and Methods.
8: Bayesian Methods in Biomedical Data Analysis.
9: Learning Classifier Systems: The Rise of Genetics-Based Machine Learning in Biomedical Data Mining.
10: Engineering Principles in Biomedical Informatics.
11: Biomedical Informatics Methods for Personalized Medicine and Participatory Health.
12: Linking Genomic and Clinical Data for Discovery and Personalized Care.
13: Putting Theory into Practice.
Appendix A: Unix Primer.
Appendix B: Ruby Primer.
Appendix C: Database Primer.
Appendix D: Web Services.
Author Index.
Subject Index.

Methods in Biomedical Informatics.css-1p8rvzy{position:absolute;padding-left:8px;}

Methods in Biomedical Informatics