Aligned-Layer Text Search in Clinical Notes

摘要：

　　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 patients 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.

关键词： Natural Language Processing Information Storage and Retrieval Electronic Health Records

作者: Stephen Wu Andrew Wen Yanshan Wang Sijia Liu Hongfang Liu

作者单位: Department of Medical Informatics and Clinical Epidemiology,Oregon Health & Science University,Portl Division of Biomedical Informatics,Mayo Clinic,Rochester,MN,USA

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 629-633

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）