Lee Minsuk, Cimino James, Zhu Hai R, Sable Carl, Shanker Vijay, Ely John, Yu Hong
Department of Health Sciences, University of Wisconsin-Milwaukee, USA.
AMIA Annu Symp Proc. 2006;2006:469-73.
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.
医生在照顾患者时会有很多问题,并且经常需要为他们的问题寻找答案。信息检索系统(例如,PubMed)通常会根据用户的查询返回一系列文档。通常返回的文档数量很多,这使得医生的信息检索“仅在下班后可行,而在临床环境中则不可行”。问答技术基于自动分析数千份电子文档,以生成针对医生提出的临床问题的简短文本答案。作者满足了医生的信息需求,并描述了医学问答系统(MedQA)的设计、实现和评估。尽管我们的长期目标是使MedQA能够回答所有类型的医学问题,但目前,我们实现MedQA是为了整合信息检索、提取和汇总技术,以自动为定义性问题(即“什么是X?”)生成段落级文本。可通过http://www.dbmi.columbia.edu/~yuh9001/research/MedQA.html访问MedQA。