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Machine Learning in Healthcare Informatics

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Cover of 'Machine Learning in Healthcare Informatics'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Machine Learning in Healthcare Informatics
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    Chapter 2 Wavelet-based Machine Learning Techniques for ECG Signal Analysis
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    Chapter 3 Application of Fuzzy Logic Control for Regulation of Glucose Level of Diabetic Patient
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    Chapter 4 The Application of Genetic Algorithm for Unsupervised Classification of ECG
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    Chapter 5 Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer
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    Chapter 6 Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference
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    Chapter 7 Rule Learning in Healthcare and Health Services Research
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    Chapter 8 Machine Learning Techniques for AD/MCI Diagnosis and Prognosis
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    Chapter 9 Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond “Black-Box” Predictions
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    Chapter 10 Clinical Utility of Machine Learning and Longitudinal EHR Data
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    Chapter 11 Rule-based Computer Aided Decision Making for Traumatic Brain Injuries
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    Chapter 12 Supervised Learning Methods for Fraud Detection in Healthcare Insurance
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    Chapter 13 Feature Extraction by Quick Reduction Algorithm: Assessing the Neurovascular Pattern of Migraine Sufferers from NIRS Signals
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    Chapter 14 A Selection and Reduction Approach for the Optimization of Ultrasound Carotid Artery Images Segmentation
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Machine Learning in Healthcare Informatics
Published by
Springer Berlin Heidelberg, December 2013
DOI 10.1007/978-3-642-40017-9
978-3-64-240016-2, 978-3-64-240017-9, 978-3-66-250763-6

Dua, Sumeet, Acharya, U. Rajendra, Dua, Prerna

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 2 1%
Brazil 2 1%
Australia 1 <1%
Hong Kong 1 <1%
Switzerland 1 <1%
Singapore 1 <1%
Sweden 1 <1%
China 1 <1%
United States 1 <1%
Other 0 0%
Unknown 181 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 22%
Student > Master 42 22%
Researcher 35 18%
Student > Bachelor 15 8%
Student > Doctoral Student 11 6%
Other 29 15%
Unknown 17 9%
Readers by discipline Count As %
Computer Science 66 34%
Engineering 45 23%
Medicine and Dentistry 10 5%
Business, Management and Accounting 7 4%
Agricultural and Biological Sciences 7 4%
Other 32 17%
Unknown 25 13%