http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Modern computational approaches to traditional chinese medicine
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. Interdisciplinary book bringing together Traditional Chinese Medicine and computer scientists. Introduces novel network technologies to Traditional Chinese Medicine informaticsProvides theory and practical examples and case studies of new techniques.
Modern Computational Approaches To Traditional Chinese Medicine -- Copyright Page -- 1. Overview of Knowledge Discovery in Traditional Chinese Medicine -- 1.1. Introduction -- 1.2. The State of the Art of TCM Data Resources -- 1.2.1. Traditional Chinese Medical Literature Analysis and Retrieval System -- 1.2.2. Figures and Photographs of Traditional Chinese Drug Database -- 1.2.3. Database of Chinese Medical Formulae -- 1.2.4. Database of Chemical Composition from Chinese Herbal Medicine -- 1.2.5. Clinical Medicine Database.
1.2.6. TCM Electronic Medical Record Database -- 1.3. Review of KDTCM Research -- 1.3.1. Knowledge Discovery for CMF Research -- 1.3.2. Knowledge Discovery for CHM Research -- 1.3.2. 1 KDD for the Research of CHM Characteristics -- 1.3.2. 2 KDD for the Research of CHM Chemical Compositions -- 1.3.3. Knowledge Discovery for Research of TCM Syndrome -- 1.3.4. Knowledge Discovery for TCM Clinical Diagnosis -- 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 -- 2.4.1. Bubble-Bootstrapping Method -- 2.4.1. 1 Pattern Definition -- 2.4.1. 2 Bubble-Bootstrapping Algorithm -- 2.4.2. Relation Weight Computing -- 2.5. MeDisco/3S System -- 2.6. Results -- 2.6.1. Functional Gene Networks -- 2.6.2. Functional Analysis of Genes from Syndrome Perspective -- 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 -- 3.3.1. MRPF Framework -- 3.3.2. Neighbor Vertices Finding and Pattern Initialization -- 3.3.3. Pattern Extension -- 3.3.4. Frequency Computing -- 3.4. Application to Prescription Compatibility Structure Detection -- 3.4.1. Motifs Detection Results -- 3.4.2. Performance Analysis -- 3.5. Conclusions -- 4. Data Quality for Knowledge Discovery in Traditional Chinese Medicine -- 4.1. Introduction -- 4.2. Key Data Quality Dimensions in TCM -- 4.2.1. Representation Granularity -- 4.2.2. Representation Consistency -- 4.2.3. Completeness -- 4.3. Methods to Handle Data Quality Problems.
4.3.1. Handling Representation Granularity -- 4.3.2. Handling Representation Consistency -- 4.3.3. Handling Completeness -- 4.4. Conclusions -- 5. Service-Oriented Data Mining in Traditional Chinese Medicine -- 5.1. Introduction -- 5.2. Related Work -- 5.2.1. Traditional Data Mining Software -- 5.2.2. Data Mining Systems for Specific Field -- 5.2.3. Distributed Data Mining Platform -- 5.2.4. The Spora Demo -- 5.3. System Architecture and Data Mining Service -- 5.3.1. Hierarchical Structure -- 5.3.1. 1 Resource Layer -- 5.3.1. 2 Component Layer -- 5.3.1. 3 Service Layer -- 5.3.1. 4 Application Layer.
[edited by] Zhaohuo Wu, Huajun Chen, Xiaohong Jiang.
[Place of publication not identified] : Elsevier, 2012
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