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Machine Learning in Healthcare Informatics / edited by Sumeet Dua, U. Rajendra Acharya, Prerna Dua.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Intelligent Systems Reference Library ; 56Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Descripción: xii, 332 páginas 119 ilustraciones, 50 ilustraciones en color. recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783642400179
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • Q342
Recursos en línea:
Contenidos:
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.
Resumen: 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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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.

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