Please use this identifier to cite or link to this item:
|Title:||Automated knowledge extraction for decision model construction: a data mining approach.|
|Citation:||Zhu, A.L.,Li, J.,Leong, T.Y. (2003). Automated knowledge extraction for decision model construction: a data mining approach.. AMIA . Annual Symposium proceedings [electronic resource] / AMIA Symposium. AMIA Symposium : 758-762. ScholarBank@NUS Repository.|
|Abstract:||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.|
|Source Title:||AMIA . Annual Symposium proceedings [electronic resource] / AMIA Symposium. AMIA Symposium|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 20, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.