Wu Stephen, Wen Andrew, Wang Yanshan, Liu Sijia, Liu Hongfang
Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
Division of Biomedical Informatics, Mayo Clinic, Rochester, MN, USA.
Stud Health Technol Inform. 2017;245:629-633.
Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient's health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries, and customize the desired degree of linguistic granularity.
临床文本中的搜索技术需要进行细粒度的语义区分，因为医学术语可能被否定、涉及患者以外的其他人或当前时间以外的某个时间。虽然自然语言处理（NLP）方法可以处理这些细粒度的区分，但从电子健康记录（EHR）中识别患者队列这样的任务同时需要将文本证据与每个患者健康记录的结构化数据进行更粗粒度的组合。因此，我们引入了对齐层语言模型，这是一种结合其他NLP系统输出的新型信息检索（IR）方法。我们表明，该框架能够表示标准的IR查询、制定以前不可能的多层查询，并定制所需的语言粒度程度。
