title: Locative inferences in medical texts.
abstract: Medical research relies on epidemiological studies conducted on a large set of clinical records that have been collected from physicians recording individual patient observations. These clinical records are recorded for the purpose of individual care of the patient with little consideration for their use by a biostatistician interested in studying a disease over a large population. Natural language processing of clinical records for epidemiological studies must deal with temporal, locative, and conceptual issues. This makes text understanding and data extraction of clinical records an excellent area for applied research. While much has been done in making temporal or conceptual inferences in medical texts, parallel work in locative inferences has not been done. This paper examines the locative inferences as well as the integration of temporal, locative, and conceptual issues in the clinical record understanding domain by presenting an application that utilizes two key concepts in its parsing strategy--a knowledge-based parsing strategy and a minimal lexicon.
authors: Department of Industrial Engineering, Texas A&M University, College Station 77843.
authors: P S
authors: PS
authors: Mayer
authors: G H
authors: GH
authors: Bailey
authors: R J
authors: RJ
authors: Mayer
authors: A
authors: A
authors: Hillis
authors: J E
authors: JE
authors: Dvoracek
mesh_terms: D001185:Artificial Intelligence; D003628:Database Management Systems; D008499:Medical Records; D009323:Natural Language Processing; D012984:Software