Machine Learning in Healthcare Informatics available in Hardcover
- Pub. Date:
- Springer Berlin Heidelberg
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.
Table of ContentsIntroduction 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.