title: Beyond information retrieval--medical question answering.
abstract: Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user's query. Frequently the number of returned documents is large and makes physicians' information seeking "practical only 'after hours' and not in the clinical settings". Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians' information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we implemented MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., "What is X?"). MedQA can be accessed at http://www.dbmi.columbia.edu/~yuh9001/research/MedQA.html.
authors.affiliation: Department of Health Sciences, University of Wisconsin-Milwaukee, USA.
authors.forename: Minsuk
authors.initials: M
authors.lastname: Lee
authors.forename: James
authors.initials: J
authors.lastname: Cimino
authors.forename: Hai R
authors.initials: HR
authors.lastname: Zhu
authors.forename: Carl
authors.initials: C
authors.lastname: Sable
authors.forename: Vijay
authors.initials: V
authors.lastname: Shanker
authors.forename: John
authors.initials: J
authors.lastname: Ely
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D003661:Decision Support Techniques; D005103:Expert Systems; D006801:Humans; D016247:Information Storage and Retrieval; D020407:Internet; D016239:MEDLINE; D009323:Natural Language Processing; D010820:Physicians; D010865:Pilot Projects