Automated Knowledge Extraction for Decision Model Construction: A Data Mining Approach.
Conference Proceeding Article
Combinations of Medical Subject Headings (MeSH) and Subheadings in MEDLINE citations may be used to infer relationships among medical concepts. To facilitate clinical decision model construction, we propose an approach to automatically extract semantic relations among medical terms from MEDLINE citations. We use the Apriori association rule mining algorithm to generate the co-occurrences of medical concepts, which are then filtered through a set of predefined semantic templates to instantiate useful relations. From such semantic relations, decision elements and possible relationships among them may be derived for clinical decision model construction. To evaluate the proposed method, we have conducted a case study in colorectal cancer management; preliminary results have shown that useful causal relations and decision alternatives can be extracted.
Databases and Information Systems | Data Storage Systems
Data Management and Analytics; Intelligent Systems and Decision Analytics
American Medical Informatics Association Annual Fall Symposium (AMIA) Proceedings
Bethesda, MD : AMIA
City or Country
Washington DC, USA
Zhu A., Li J., and Tze-Yun LEONG.
Automated Knowledge Extraction for Decision Model Construction: A Data Mining Approach.. (2003). American Medical Informatics Association Annual Fall Symposium (AMIA) Proceedings. 758-762. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2994