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 scientistsIntroduces novel network technologies to Traditional Chinese Medicine informaticsProvides theory and practical examples and case studies of new techniques
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| Overview on Knowledge Discovery in Traditional Chinese Medicine | |
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| Integrative Mining of Traditional Chinese Medicine Literature and MEDLINE for Functional Gene Networks | |
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| MapReduce-based Network Motif Detection for Traditional Chinese Medicine | |
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| Data Quality for Knowledge Discovery in Traditional Chinese Medicine | |
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| Service-oriented Data Mining in Traditional Chinese Medicine | |
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| Semantic E-Science for Traditional Chinese Medicine | |
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| Ontology Development for Unified Traditional Chinese Medical Language System | |
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| Causal Knowledge Modeling for Traditional Chinese Medicine Using OWL | |
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| Dynamic Sub-ontology Evolution for Traditional Chinese Medicine Web Ontology | |
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| Semantic Association Mining for Traditional Chinese Medicine | |
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| Semantic-based Database Integration for Traditional Chinese Medicine | |
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| Probabilistic Semantic Relation Discovery from Traditional Chinese Medical Literatures | |
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| Deriving Similarity Graphs from Traditional Chinese Medicine Linked Data on Semantic Web | |
Madeleine Pullman is an Associate Professor of Operations Management. She earned her Ph.D. in business administration at the University of Utah in 1997. She has previously taught in Graduate and Executive programs at Cornell University, London Business School, Southern Methodist University, CSU, CU, and University of Utah. Her articles have appeared in various journals including Journal of Operations Management, Decision Sciences, Production and Operations Management, Journal of Service Research, International Journal of Service Industry Management, Cornell Hotel and Restaurant Administration Quarterly, Omega, and Journal of Product Innovation Management.Zhaohui Wu is an Associate Professor of Supply Chain and Operations Management. He is currently teaching and conducting research on supply networks, buyer-supplier relationships and environmental management strategy in supply chain operations. Zhaohui worked as a buyer at Lord Corporation, an U.S. aerospace company, and as a project manager at CMEC, a Chinese international trade company.
