Semi-Automatic Mark-Up and UMLS Annotation of Clinical Guidelines

摘要：

　　Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization in the healthcare domain. To derive clinical pathways from clinical guidelines, the imprecise, non-formalized abstract guidelines must be formalized. The transfer of evidence-based knowledge (clinical guidelines) to care processes (clinical pathways) is not straightforward due to different information contents and semantical constructs. A complex step within this formalization process is the mark-up step and annotation of the text passages to terminologies. The Unified Medical Language System (UMLS) provides a common reference terminology as well as the semantic link for combining the clinical pathways to patient-specific information. This paper proposes a semi-automated mark-up and UMLS annotation for clinical guidelines by using natural language processing techniques. The algorithm has been tested and evaluated using a German breast cancer guideline.

关键词： Decision Support Systems,Clinical Natural Language Processing Machine Learning

作者: Matthias Becker Britta B(o)ckmanna

作者单位: Department of Medical Informatics,University of Applied Sciences and Arts,Dortmund,Germany Department of Medical Informatics,University of Applied Sciences and Arts,Dortmund,Germany;IMIBE,Uni

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: 294-297

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）