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Data Analytics for Traditional Chinese Medicine Research

Overview of attention for book
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
Attention for Chapter 5: Prescription Analysis and Mining
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Citations

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Chapter title
Prescription Analysis and Mining
Chapter number 5
Book title
Data Analytics for Traditional Chinese Medicine Research
Published by
Springer International Publishing, January 2014
DOI 10.1007/978-3-319-03801-8_5
Book ISBNs
978-3-31-903800-1, 978-3-31-903801-8
Authors

Guang Zheng, Miao Jiang, Cheng Lu, Aiping Lu

Editors

Josiah Poon, Simon K. Poon

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 1 33%
Student > Master 1 33%
Unknown 1 33%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 33%
Computer Science 1 33%
Unknown 1 33%