U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
J Biomed Inform. 2009 Oct;42(5):760-72. doi: 10.1016/j.jbi.2009.08.007. Epub 2009 Aug 13.
Computerized clinical decision support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. natural language processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.
计算机化临床决策支持（CDS）旨在通过在需要时提供易于访问的健康相关信息来帮助医疗保健提供者和公众做出决策。自然语言处理（NLP）在使用自由文本信息来驱动 CDS 方面起着重要作用，它代表了临床知识和 CDS 干预措施的标准化格式，并利用了临床叙述。临床叙述的早期创新 NLP 研究之后是在主要临床中心进行的稳定研究阶段，以及主流兴趣向生物医学 NLP 的转移。本综述主要关注最近重新兴起的对开发基本 NLP 方法和 CDS 中 NLP 系统的研究进展的兴趣。讨论了针对不同子语言、目标用户群体和支持目标所提出的挑战的当前解决方案。
