A study of biomedical concept identification: MetaMap vs. people.
Although huge amounts of unstructured text are available as a rich source of biomedical knowledge, to process this unstructured knowledge requires tools that identify concepts from free-form text. MetaMap is one tool that system developers in biomedicine have commonly used for such a task, but few have studied how well it accomplishes this task in general. In this paper, we report on a study that compares MetaMap's performance against that of six people. Such studies are challenging because the task is inherently subjective and establishing consensus is difficult. Nonetheless, for those concepts that subjects generally agreed on, MetaMap was able to identify most concepts, if they were represented in the UMLS. However, MetaMap identified many other concepts that peo-ple did not. We also report on our analysis of the types of failures that MetaMap exhibited as well as trends in the way people chose to identify concepts.
Pratt
Wanda
W
Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, USA.
Yetisgen-Yildiz
Meliha
M
D000043:Abstracting and Indexing / Q000379:methods*; D006801:Humans; D016239:MEDLINE; D009323:Natural Language Processing; D017432:Unified Medical Language System