Machine Learning in Healthcare Informatics /
Dua, Sumeet.
Machine Learning in Healthcare Informatics / edited by Sumeet Dua, U. Rajendra Acharya, Prerna Dua. - xii, 332 páginas 119 ilustraciones, 50 ilustraciones en color. recurso en línea. - Intelligent Systems Reference Library, 56 1868-4394 ; .
Springer eBooks
From the Contents -- 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 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.
9783642400179
10.1007/9783642400179 doi
Q342
Machine Learning in Healthcare Informatics / edited by Sumeet Dua, U. Rajendra Acharya, Prerna Dua. - xii, 332 páginas 119 ilustraciones, 50 ilustraciones en color. recurso en línea. - Intelligent Systems Reference Library, 56 1868-4394 ; .
Springer eBooks
From the Contents -- 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 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.
9783642400179
10.1007/9783642400179 doi
Q342