AI&TCM 2017 - 1st IEEE International Workshop on Artificial Intelligence in Chinese Medicine Workshop (AI&TCM 2017)

The 1st IEEE International Workshop on Artificial
Intelligence in Chinese Medicine Workshop (AI&TCM
2017)
Dalian, China, 12-15 October 2017
Workshop Chairs:
Siwei Yu, Wuhan University, China, manfisy-AT-163.com
Tong Yu, Information on Traditional Chinese Medicine, China Academy of Chinese
Medical Sciences, China, yutongoracle-AT-hotmail.com
SCOPE
A strategy of traditional medicine (TM) is to establish a knowledge infrastructure for knowledge-based policy-making, clinical decision-making，education, and research. We need to increase the availability of e-library, datasets, and knowledge bases through the Internet and other media. Also, Artificial intelligence and TM need to be combined to implement key technologies such as intelligent diagnosis and prescription recommendation.
The application of AI in traditional Chinese medicine (TCM) can be traced back to 1980's when the primary focus is on the development of expert systems, which brought out many research issues such as the automation of TCM diagnosis, TCM knowledge representation and reasoning, and TCM knowledge engineering. Recently, as the AI field made tremendous progress (e.g., breakthrough technologies such as deep learning and successful medical applications), the research of AI in TCM ushered in a new climax. For example, deep learning and image recognition technologies were used for intelligent diagnosis; e-libraries, ontologies, knowledge graphs and other large-scale repositories were constructed for knowledge services; KDD methods were used to gain insights from the TCM big data. AI in TCM will play a major role in the implementation of traditional medicine development strategy, but there is still a lot of R & D work to do. It is important is to attract the attention of experts from both TCM and IT and to strengthen cross-disciplinary collaboration.
Given this, we intend to organize a workshop on Artificial Intelligence in Chinese Medicine, focused on the latest research and the most challenging problems in this field. For example, how to use AI technology such as image recognition to achieve intelligent diagnosis; how to establish large-scale knowledge system for effective knowledge organization; and how to implement intelligent services such as intelligent search, clinical decision support, and health knowledge recommendation.
The topics may include:
• Analysis and simulation of the TCM thinking mechanism
• TCM knowledge representation and reasoning
• Analysis and organization of the TCM knowledge system
• Acquisition, integration, and sharing of TCM knowledge
• Ontologies, knowledge graphs, and other large-scale repositories for TCM
• Knowledge engineering methods and projects (such as crowdsourcing, data wiki, etc.)
• Sustainable development mechanisms of TCM knowledge systems, such as automatic updating, self-learning, and self-evolution
• Digitization methods of TCM ancient books, medical records, scientific records and other information sources
• Speech recognition for TCM clinic (such as case record input)
• Theory and methodology of TCM big data
• Collection and processing of TCM big data
• Analysis and utilization of TCM big data
• TCM text (especially classics) mining
• Analysis and mining of TCM case records
• The sharing and public service of TCM big data
• Practical systems and applications of TCM big data
• Automation of the four TCM diagnostic methods
• Intelligent diagnosis based on image recognition
• Analysis and Simulation of TCM syndrome differentiation and treatment
• Intelligent search for TCM knowledge
• Intelligent recommendation of TCM prescription and other knowledge
• TCM clinical decision-support systems
• TCM knowledge service and intelligent consulting systems (such as cloud platforms, mobile APPs)
• Successful use cases of AI and TCM for difficult diseases, chronic diseases, and major diseases
• TCM informatics standardization (standards and technical specifications)