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http://dx.doi.org/10.3745/KTSDE.2022.11.1.35

Disease Prediction By Learning Clinical Concept Relations  

Jo, Seung-Hyeon (전북대학교 컴퓨터공학과)
Lee, Kyung-Soon (전북대학교 컴퓨터공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.1, 2022 , pp. 35-40 More about this Journal
Abstract
In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.
Keywords
Cninical Decision Support; Clinical Concept Relation; Deep Learning; Disaese Prediction; Query Expansion;
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