An Emotion-fused Medical Knowledge Graph and its Application in Decision Support

Traditional medical guidance becomes increasingly unsatisfactory, as the care of patients should be centered around not just clinical symptoms but also their values and preferences. A method is proposed, in this paper, to fuse clinical knowledge and patient preferences into an integrated knowledge g...

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Bibliographic Details
Published in2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) pp. 1381 - 1388
Main Authors Liu, Zhang, Xiao, Liang, Chen, Jianxia, Yu, He, Ye, Yunlong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
Analytical models
Computational modeling
Conferences
Decision making
Fuses
information extraction
knowledge graph
patient reviews
Prototypes
Sentiment analysis
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Summary:Traditional medical guidance becomes increasingly unsatisfactory, as the care of patients should be centered around not just clinical symptoms but also their values and preferences. A method is proposed, in this paper, to fuse clinical knowledge and patient preferences into an integrated knowledge graph. Objective data was extracted from semi-structured online medical service interfaces, and subjective emotional data from patient review pages. A prototype system was designed and implemented to demonstrate the feasibility of the method. The system can recommend a ranked list of doctors with the best matched clinical background as well as patient preferences. An evaluation was conducted via carrying out a survey of user groups upon the medical guidance options of a human nurse, the "We Doctor" system, and our prototype system.
DOI:10.1109/COMPSAC54236.2022.00218