Machine Learning in Healthcare Informatics
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Machine Learning in Healthcare Informatics

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ISBN-13:
9783642400162
Einband:
Buch
Erscheinungsdatum:
01.01.2014
Seiten:
332
Autor:
Sumeet Dua
Gewicht:
652 g
Format:
242x165x25 mm
Serie:
56, Intelligent Systems Reference Library
Sprache:
Englisch
Beschreibung:

Providing a unique and integrated compendium of both current and emerging machine-learning paradigms in the vital field of health informatics, this work by leading experts reflects the diversity and complexity of this multi-disciplinary area of research.
Providing a unique and integrated compendium of both current and emerging machine-learning paradigms in the vital field of health informatics, this work by leading experts reflects the diversity and complexity of this multi-disciplinary area of research.
Introduction to Machine Learning in Healthcare Informatics.- Wavelet-Based Machine Learning Techniques for ECG Signal Analysis.- Application of Fuzzy Logic Control for Regulation of Glucose Level of Diabetic Patient.- A Study on Machine Learning in EEG Signal Analysis.- The Application of Genetic Algorithm for Unsupervised Classification of ECG.- Pixel-based Machine Learning in Computer-aided Diagnosis of Lung and Colon Cancer.- Understanding foot function during stance phase by Bayesian Network based causal inference.- Rule Learning in Healthcare and Health Services Research.- Machine Learning Techniques for AD/MCI Diagnosis and Prognosis.- Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond "Black-Box" Predictions.- Techniques for AD/MCI Diagnosis and Prognosis.
The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

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