Machine Learning in Healthcare Informatics, 1st Edition

  • Published By:
  • ISBN-10: 3642400175
  • ISBN-13: 9783642400179
  • DDC: 006.31
  • Grade Level Range: College Freshman - College Senior
  • 332 Pages | eBook
  • Original Copyright 2014 | Published/Released June 2014
  • This publication's content originally published in print form: 2014

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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 Contents

Front Cover.
Editorial Board.
Other Frontmatter.
Title Page.
Copyright Page.
1: Introduction to Machine Learning in Healthcare Informatics.
2: Wavelet-based Machine Learning Techniques for ECG Signal Analysis.
3: Application of Fuzzy Logic Control for Regulation of Glucose Level of Diabetic Patient.
4: The Application of Genetic Algorithm for Unsupervised Classification of ECG.
5: Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer.
6: Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference.
7: Rule Learning in Healthcare and Health Services Research.
8: Machine Learning Techniques for AD/MCI Diagnosis and Prognosis.
9: Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond “Black-Box” Predictions.
10: Clinical Utility of Machine Learning and Longitudinal EHR Data.
11: Rule-based Computer Aided Decision Making for Traumatic Brain Injuries.
12: Supervised Learning Methods for Fraud Detection in Healthcare Insurance.
13: Feature Extraction by Quick Reduction Algorithm: Assessing the Neurovascular Pattern of Migraine Sufferers from NIRS Signals.
14: A Selection and Reduction Approach for the Optimization of Ultrasound Carotid Artery Images Segmentation.