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Data Management and Analytics for Medicine and Healthcare

Overview of attention for book
Cover of 'Data Management and Analytics for Medicine and Healthcare'

Table of Contents

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    Book Overview
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    Chapter 1 How Blockchain Could Empower eHealth: An Application for Radiation Oncology
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    Chapter 2 On-Demand Service-Based Big Data Integration: Optimized for Research Collaboration
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    Chapter 3 CHIPS – A Service for Collecting, Organizing, Processing, and Sharing Medical Image Data in the Cloud
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    Chapter 4 High Performance Merging of Massive Data from Genome-Wide Association Studies
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    Chapter 5 An Emerging Role for Polystores in Precision Medicine
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    Chapter 6 Social Media Mining to Understand Public Mental Health
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    Chapter 7 Effects of Varying Sampling Frequency on the Analysis of Continuous ECG Data Streams
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    Chapter 8 Detection and Visualization of Variants in Typical Medical Treatment Sequences
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    Chapter 9 Umedicine: A System for Clinical Practice Support and Data Analysis
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    Chapter 10 Association Rule Learning and Frequent Sequence Mining of Cancer Diagnoses in New York State
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    Chapter 11 Healthsurance – Mobile App for Standardized Electronic Health Records Database
Attention for Chapter 4: High Performance Merging of Massive Data from Genome-Wide Association Studies
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Chapter title
High Performance Merging of Massive Data from Genome-Wide Association Studies
Chapter number 4
Book title
Data Management and Analytics for Medicine and Healthcare
Published by
Springer, Cham, September 2017
DOI 10.1007/978-3-319-67186-4_4
Book ISBNs
978-3-31-967185-7, 978-3-31-967186-4
Authors

Xiaobo Sun, Fusheng Wang, Zhaohui Qin

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 100%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 100%

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