Modern Computational Approaches to Traditional Chinese Medicine

Modern Computational Approaches to Traditional Chinese Medicine

1st Edition - July 13, 2012

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  • Editors: Zhaohui Wu, Huajun Chen, Xiaohong Jiang
  • Paperback ISBN: 9780323282727
  • eBook ISBN: 9780123985194

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Recognized as an essential component of Chinese culture, Traditional Chinese Medicine (TCM) is both an ancient medical system and one still used widely in China today. TCM’s independently evolved knowledge system is expressed mainly in the Chinese language and the information is frequently only available through ancient classics and confidential family records, making it difficult to utilize. The major concern in TCM is how to consolidate and integrate the data, enabling efficient retrieval and discovery of novel knowledge from the dispersed data. Computational approaches such as data mining, semantic reasoning and computational intelligence have emerged as innovative approaches for the reservation and utilization of this knowledge system. Typically, this requires an inter-disciplinary approach involving Chinese culture, computer science, modern healthcare and life sciences. This book examines the computerization of TCM information and knowledge to provide intelligent resources and supporting evidences for clinical decision-making, drug discovery, and education. Recent research results from the Traditional Chinese Medicine Informatics Group of Zhejiang University are presented, gathering in one resource systematic approaches for massive data processing in TCM. These include the utilization of modern Semantic Web and data mining methods for more advanced data integration, data analysis and integrative knowledge discovery. This book will appeal to medical professionals, life sciences students, computer scientists, and those interested in integrative, complementary, and alternative medicine.

Key Features

  • Interdisciplinary book bringing together Traditional Chinese Medicine and computer scientists
  • Introduces novel network technologies to Traditional Chinese Medicine informatics
  • Provides theory and practical examples and case studies of new techniques


Researchers and professionals working in medical fields and computer science

Table of Contents

  • Preface

    List of Contributors

    1. Overview of Knowledge Discovery in Traditional Chinese Medicine

    1.1 Introduction

    1.2 The State of the Art of TCM Data Resources

    1.3 Review of KDTCM Research

    1.4 Discussions and Future Directions

    1.5 Conclusions


    2. Integrative Mining of Traditional Chinese Medicine Literature and MEDLINE for Functional Gene Networks

    2.1 Introduction

    2.2 Connecting TCM Syndrome to Modern Biomedicine by Integrative Literature Mining

    2.3 Related Work on Biomedical Literature Mining

    2.4 Name Entity and Relation Extraction Methods

    2.5 MeDisco/3S System

    2.6 Results

    2.7 Conclusions


    3. MapReduce-Based Network Motif Detection for Traditional Chinese Medicine

    3.1 Introduction

    3.2 Related Work

    3.3 MapReduce-Based Pattern Finding

    3.4 Application to Prescription Compatibility Structure Detection

    3.5 Conclusions


    4. Data Quality for Knowledge Discovery in Traditional Chinese Medicine

    4.1 Introduction

    4.2 Key Data Quality Dimensions in TCM

    4.3 Methods to Handle Data Quality Problems

    4.4 Conclusions


    5. Service-Oriented Data Mining in Traditional Chinese Medicine

    5.1 Introduction

    5.2 Related Work

    5.3 System Architecture and Data Mining Service

    5.4 Case Studies

    5.5 Conclusions


    6. Semantic E-Science for Traditional Chinese Medicine

    6.1 Introduction

    6.2 Results

    6.3 Discussion

    6.4 Conclusions

    6.5 Methods


    7. Ontology Development for Unified Traditional Chinese Medical Language System

    7.1 Introduction

    7.2 The Principle and Knowledge System of TCM

    7.3 What Is an Ontology?

    7.4 Protégé 2000: The Tool We Use

    7.5 Ontology Design and Development for UTCMLS

    7.6 Results

    7.7 Conclusions


    8. Causal Knowledge Modeling for Traditional Chinese Medicine Using OWL 2

    8.1 Introduction

    8.2 Causal TCM Knowledge Modeling

    8.3 Causal Reasoning

    8.4 Evaluation

    8.5 Conclusions


    9. Dynamic Subontology Evolution for Traditional Chinese Medicine Web Ontology

    9.1 Introduction

    9.2 TCM Domain Ontology

    9.3 Subontology Model

    9.4 Ontology Cache for Knowledge Reuse

    9.5 Dynamic Subontology Evolution

    9.6 Experiment and Evaluation

    9.7 Related Work

    9.8 Conclusions


    10. Semantic Association Mining for Traditional Chinese Medicine

    10.1 Introduction

    10.2 Related Work

    10.3 Methods

    10.4 Evaluation

    10.5 Use Cases

    10.6 Conclusions


    11. Semantic-Based Database Integration for Traditional Chinese Medicine

    11.1 Introduction

    11.2 System Architecture and Technical Features

    11.3 Semantic Mediation

    11.4 TCM Semantic Portals

    11.5 User Evaluation and Lesson Learned

    11.6 Related Work

    11.7 Conclusions


    12. Probabilistic Semantic Relationship Discovery from Traditional Chinese Medical Literature

    12.1 Background

    12.2 Related Work

    12.3 Methods

    12.4 Results and Discussions

    12.5 Conclusions


    13. Deriving Similarity Graphs from Traditional Chinese Medicine Linked Data on the Semantic Web

    13.1 Introduction

    13.2 Related Work

    13.3 SST Approach

    13.4 Experiments and Results

    13.5 Conclusions


Product details

  • No. of pages: 250
  • Language: English
  • Copyright: © Elsevier 2012
  • Published: July 13, 2012
  • Imprint: Elsevier
  • Paperback ISBN: 9780323282727
  • eBook ISBN: 9780123985194

About the Editors

Zhaohui Wu

Zhaohui Wu
Department of Computer Science, Zhejiang University, Hangzhou, China

Affiliations and Expertise

Department of Computer Science, Zhejiang University, Hangzhou, China

Huajun Chen

Department of Computer Science, Zhejiang University, Hangzhou, China

Affiliations and Expertise

Department of Computer Science, Zhejiang University, Hangzhou, China

Xiaohong Jiang

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