Modern Computational Approaches to Traditional Chinese Medicine - 1st Edition - ISBN: 9780123985101, 9780123985194

Modern Computational Approaches to Traditional Chinese Medicine

1st Edition

Editors: Zhaohui Wu Huajun Chen Xiaohong Jiang
Hardcover ISBN: 9780123985101
eBook ISBN: 9780123985194
Imprint: Elsevier
Published Date: 13th July 2012
Page Count: 250
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Description

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

Readership

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

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

6. Semantic E-Science for Traditional Chinese Medicine

6.1 Introduction

6.2 Results

6.3 Discussion

6.4 Conclusions

6.5 Methods

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

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

REFERENCES

Details

No. of pages:
250
Language:
English
Copyright:
© Elsevier 2012
Published:
Imprint:
Elsevier
eBook ISBN:
9780123985194
Hardcover ISBN:
9780123985101
Paperback ISBN:
9780323282727

About the Editor

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

Reviews

"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...This book will appeal to medical professionals, life sciences students, computer scientists, and those interested in integrative, complementary, and alternative medicine."--Zentralblatt MATH 1286-1