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Data Analytics for Traditional Chinese Medicine Research

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Cover of 'Data Analytics for Traditional Chinese Medicine Research'

Table of Contents

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    Book Overview
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    Chapter 1 Searching for Evidence in Traditional Chinese Medicine Research: A Review and New Opportunities
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    Chapter 2 Causal Complexities of TCM Prescriptions: Understanding the Underlying Mechanisms of Herbal Formulation
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    Chapter 3 Medical Diagnosis by Using Machine Learning Techniques
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    Chapter 4 Network Based Deciphering of the Mechanism of TCM
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    Chapter 5 Prescription Analysis and Mining
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    Chapter 6 Statistical Validation of TCM Syndrome Postulates in the Context of Depressive Patients
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    Chapter 7 Artificial Neural Network Based Chinese Medicine Diagnosis in Decision Support Manner and Herbal Ingredient Discoveries
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    Chapter 8 Chromatographic Fingerprinting and Chemometric Techniques for Quality Control of Herb Medicines
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    Chapter 9 A New Methodology for Uncovering the Bioactive Fractions in Herbal Medicine Using the Approach of Quantitative Pattern-Activity Relationship
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    Chapter 10 An Innovative and Comprehensive Approach in Studying the Complex Synergistic Interactions Among Herbs in Chinese Herbal Formulae
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    Chapter 11 Data Mining in Real-World Traditional Chinese Medicine Clinical Data Warehouse
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    Chapter 12 TCM Data Mining and Quality Evaluation with SAPHRONTM System
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    Chapter 13 An Overview on Evidence-Based Medicine and Medical Informatics in Traditional Chinese Medicine Practice
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Citations

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Title
Data Analytics for Traditional Chinese Medicine Research
Published by
Springer International Publishing, January 2014
DOI 10.1007/978-3-319-03801-8
ISBNs
978-3-31-903800-1, 978-3-31-903801-8
Editors

Josiah Poon, Simon K. Poon

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 14 25%
Student > Master 12 21%
Student > Doctoral Student 5 9%
Researcher 4 7%
Student > Ph. D. Student 4 7%
Other 7 12%
Unknown 11 19%
Readers by discipline Count As %
Computer Science 9 16%
Medicine and Dentistry 9 16%
Pharmacology, Toxicology and Pharmaceutical Science 5 9%
Nursing and Health Professions 4 7%
Chemistry 4 7%
Other 14 25%
Unknown 12 21%