title: Generating executable knowledge for evidence-based medicine using natural language and semantic processing.
abstract: With an increase in the prevalence of patients having multiple medical conditions, along with the increasing number of medical information sources, an intelligent approach is required to integrate the answers to physicians' patient-related questions into clinical practice in the shortest, most specific way possible. Cochrane Scientific Reviews are currently considered to be the "gold standard" for evidence-based medicine (EBM), because of their well-defined systematic approach to assessing the available medical information. In order to develop semantic approaches for enabling the reuse of these Reviews, a system for producing executable knowledge was designed using a natural language processing (NLP) system we developed (BioMedLEE), and semantic processing techniques. Though BioMedLEE was not designed for or trained over the Cochrane Reviews, this study shows that disease, therapy and drug concepts can be extracted and correlated with an overall recall of 80.3%, coding precision of 94.1%, and concept-concept relationship precision of 87.3%.
authors.affiliation: Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA.
authors.forename: Tara
authors.initials: T
authors.lastname: Borlawsky
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
authors.forename: Yves A
authors.initials: YA
authors.lastname: Lussier
mesh_terms: D000465:Algorithms; D019317:Evidence-Based Medicine; D005568:Forms and Records Control; D006801:Humans; D016247:Information Storage and Retrieval; D051188:Knowledge Bases; D009323:Natural Language Processing; D012196:Review Literature as Topic; D012660:Semantics; D017432:Unified Medical Language System