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First published March 28, 2003 as JAMIA PrePrint; doi:10.1197/jamia.M1176
Journal of the American Medical Informatics Association 2003;10(4):351-362
© 2003 American Medical Informatics Association

A more recent version of this article appeared on July 1, 2003
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Submitted on June 14, 2002
Accepted on March 5, 2003

"Understanding" medical school curriculum content using KnowledgeMap

Joshua C. Denny1, Jeffrey D. Smithers1, Randolph A. Miller MD2, and Anderson Spickard III MD, MS3*

Affiliation of the authors: 1 School of Medicine, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN; 2 Department of Biomedical Informatics, Vanderbilt University, Nashville, TN; 3 Department of Medicine, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN

* To whom correspondence should be addressed.

Objective To describe the development and evaluation of computational tools to identify concepts within medical curricular documents, using information derived from the National Library of Medicine's Unified Medical Language System (UMLS). The long-term goal of the KnowledgeMap (KM) project is to provide faculty with an improved ability to develop, review, and integrate components of the medical school curriculum.

Design The KM concept identifier uses lexical resources partially derived from the UMLS (SPECIALIST lexicon and Metathesaurus), heuristic language processing techniques, and an empirical scoring algorithm. KM differentiates among potentially matching Metathesaurus concepts within a source document. The authors manually identified important gold standard biomedical concepts within selected medical school full-content lecture documents, and used these documents to compare KM concept recognition to that of a known state-of-the-art standard -- the National Library of Medicine's MetaMap program.

Measurements The number of gold standard concepts in each lecture document identified by either KM and MetaMap, and the cause of each failure or relative success in a random subset of documents.

Results For 4,281 gold standard concepts, MetaMap matched 78% and KM 82%. Precision for gold standard concepts was 85% for MetaMap and 89% for KM. KM's heuristics accurately matched acronyms, concepts underspecified in the document, and ambiguous matches. The most frequent cause of matching failures was absence of target concepts from the UMLS Metathesaurus.

Conclusion The prototypic KnowledgeMap system provided an encouraging rate of concept extraction for representative medical curricular texts. Future versions of KnowledgeMap should be evaluated for their ability to allow administrators, lecturers, and students to navigate through the medical curriculum to locate redundancies, find interrelated information, and identify omissions. In addition, KnowledgeMap's ability to meet specific, personal information needs should be assessed.






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