使用统一医学语言系统对齐结构化和非结构化医学问题。
Aligning Structured and Unstructured Medical Problems Using UMLS.
作者信息
Carlo Lorena, Chase Herbert S, Weng Chunhua
机构信息
Department of Biomedical Informatics, Columbia University, New York, NY, 10032.
出版信息
AMIA Annu Symp Proc. 2010 Nov 13;2010:91-5.
Abstract
This paper reports a pilot study to align medical problems in structured and unstructured EHR data using UMLS. A total of 120 medical problems in discharge summaries were extracted using NLP software (MedLEE) and aligned with 87 ICD-9 diagnoses for 19 non-overlapping hospital visits of 4 patients. The alignment accuracy was evaluated by a medical doctor. The average overlap of medical problems between the two data sources obtained by our automatic alignment method was 23.8%, which was about half of the manual review result, 43.56%. We discuss the implications for related research in integrating structured and unstructured EHR data.
摘要
本文报告了一项试点研究，旨在使用统一医学语言系统（UMLS）对结构化和非结构化电子健康记录（EHR）数据中的医疗问题进行对齐。使用自然语言处理软件（MedLEE）从出院小结中提取了总共120个医疗问题，并将其与4名患者19次不重叠住院就诊的87个国际疾病分类第九版（ICD-9）诊断进行了对齐。由一名医生评估对齐的准确性。通过我们的自动对齐方法获得的两个数据源之间医疗问题的平均重叠率为23.8%，约为人工审核结果（43.56%）的一半。我们讨论了对整合结构化和非结构化EHR数据相关研究的启示。
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