Approaches on measuring the uncertainty of medical knowledge

Corresponding author: dujian, windowsdujian@163.com

DOI: 10.12201/bmr.202202.00001

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  Abstract: In DIKW paradigm, from knowledge to wisdom, the problem to be solved is how to make the best decision under the condition of uncertainty. This paper combines the textual uncertainty knowledge (evidence) with structured knowledge graph. We put forward several methods to calculate the uncertainty level of medical knowledge by focusing on translating text to computable data, including scale, probability, information entropy, evidence comment network and so on. It is proposed that the decision can be made by the machine for the knowledge with higher certainty level. For such conditions where there is only evidence with lower certainty level, it is important to join machines and doctors (scientists) together for shared decision-making so as to improve the efficiency of knowledge-driven decision support.

  Key words: Uncertainty；Scientific knowledge；Probability；Information entropy；Evidence comment network

  Submit time: 1 March 2022

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2022-02-20	bmr.202202.00001V1	Download

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