Disease Prediction By Learning Clinical Concept Relations


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 1, pp. 35-40, Jan. 2022
https://doi.org/10.3745/KTSDE.2022.11.1.35,   PDF Download:
Keywords: Cninical Decision Support, Clinical Concept Relation, Deep Learning, Disaese Prediction, Query Expansion
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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
S. Jo and K. Lee, "Disease Prediction By Learning Clinical Concept Relations," KIPS Transactions on Software and Data Engineering, vol. 11, no. 1, pp. 35-40, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.1.35.

[ACM Style]
Seung-Hyeon Jo and Kyung-Soon Lee. 2022. Disease Prediction By Learning Clinical Concept Relations. KIPS Transactions on Software and Data Engineering, 11, 1, (2022), 35-40. DOI: https://doi.org/10.3745/KTSDE.2022.11.1.35.