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Evidence-Based Patient Classification for Traditional Chinese Medicine

Call for Papers

Effective use of traditional Chinese medicine (TCM) treatments requires syndrome differentiation where patients suffering from a western medicine disease are divided into several classes based on their symptoms and signs, and different TCM treatments are applied to patients in different proper patient classification which is important not only for clinic uniformity and efficacy, but also for research endeavors to evaluate syndrome-oriented treatment plans or to gain biomedical understanding of various syndrome types.

TCM patient classification is a challenging problem as there are no gold standards to rely on. Nonetheless, fruitful investigations have been conducted on several fronts. Potential topics include, but are not limited to:

  • Establishment of classification standards for particular diseases
  • Extraction of classification rules from data labeled by experts
  • Detection of symptom cooccurrence patterns from unlabeled data and use of such patterns to define syndrome types
  • Evidence-based pulse and tongue classification
  • Biomedical research on particular syndrome types

This special issue aims at showing the state of the art of research on evidence-based TCM patient classification. We welcome original research articles as well as review articles on the topics listed above and other related issues.

Before submission, authors should carefully read the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/ecam/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/ecam/class/ according to the following timetable:

Manuscript DueFriday, 5 September 2014
First Round of ReviewsFriday, 28 November 2014
Publication DateFriday, 23 January 2015

Lead Guest Editor

  • Nevin L. Zhang, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Guest Editors

  • Ka-Fai Chung, Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong
  • Guo-Zheng Li, Department of Control Science & Engineering, Tongji University, Shanghai, China
  • Josiah Poon, School of Information Technologies, The University of Sydney, Sydney, Australia