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Machine Learning in Healthcare Informatics

ISRL: MACHINE LEARNING IN HEALTHCARE INFORMATICS

Medical Informatics

Health, Medicine & Nursing

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

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.

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Editorial Board.
Other Frontmatter.
Title Page.
Copyright Page.
Preface.
Contents.
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 &#x201c;Black-Box&#x201d; 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.

Machine Learning in Healthcare Informatics.css-1p8rvzy{position:absolute;padding-left:8px;}

Machine Learning in Healthcare Informatics