On The Automation of Medical Knowledge and Medical Decision Support Systems
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This chapter follows the steps undertaken by other researchers in the field of knowledge engineering in Medicine. The material here presented is concerned with four key issues: The nature of the medical knowledge, the characteristics of the reasoning processes in medicine, the automatic acquisition of medical knowledge, and the effective handling of uncertain medical knowledge. The chapter reviews first categorical logic models and Bayesian methods. From a first conclusion on inherent uncertainty of medical decision making and reasoning, a vector representation of medical knowledge is proposed. This vector representation facilitates automation of the processes involved in the development of medical decision support systems. The use of contingency tables for automatic and objective knowledge acquisition is also proposed. To facilitate the understanding of the presented material, we have tried to illustrate each statement, idea, proposal or approach, with examples taken from the literature. Finally, the chapter concludes with an analysis of the possibilities of the overall method. Major potential contributions are justified, explained and discussed. The mentioned analysis focus on the application of the proposed approach on a simplified clinical case derived from the experience of the authors in the domain of the Sleep Medicine. Finally, the chapter concludes with a discussion, and with the establishment of the required conclusions.
KeywordsArtificial intelligence in medicine Knowledge engineering Uncertain reasoning Knowledge representation Medical decision support systems
This work has-been supported by Spanish regional government of Galicia (Groups of Excellence Program GRC2014/035) and by Spanish MINECO (Research Project TIN2013-40686), co-funded by European ERDF. Special thanks to Case Western Reserve University for permitting us to use Their Sleep Heart Health Study database.
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