Contextual models of clinical publications for enhancing retrieval from full-text databases.
Conventional methods for retrieving information from the medical literature are imprecise and inefficient. Information retrieval systems employ unmanageable indexing vocabularies or use full-text representations that overwhelm the user with irrelevant information. This paper describes a document representation designed to improve the precision of searching in textual databases without significantly compromising recall. The representation augments simple text word representations with contextual models that reflect recurring semantic themes in clinical publications. Using this representation, a searcher may indicate both the terms of interest and the contexts in which they should occur. The contexts limit the potential interpretations of text words, and thus form the basis for more precise searching. In this paper, we discuss the shortcomings of traditional retrieval systems and describe our context-based representation. Improved retrieval performance with contextual models is illustrated by example, and a more extensive study is proposed. We present an evaluation of the contextual models as an indexing scheme, using a variation of the traditional inter-indexer consistency experiments, and we demonstrate that contextual indexing is reproducible by minimally trained physicians and medical students.
Purcell
G P
GP
Section on Medical Informatics, Stanford University School of Medicine, California 94305-5479, USA.
Shortliffe
E H
EH
D000043:Abstracting and Indexing / Q000379:methods*; D005069:Evaluation Studies as Topic; D016247:Information Storage and Retrieval; D007256:Information Systems; D008962:Models, Theoretical; D018875:Vocabulary, Controlled