General Symptom Extraction from VA Electronic Medical Notes

摘要：

　　There is need for cataloging signs and symptoms, but not all are documented in structured data. The text from clinical records are an additional source of signs and symptoms. We describe a Natural Language Processing (NLP) technique to identify symptoms from text. Using a human-annotated reference corpus from VA electronic medical notes we trained and tested an NLP pipeline to identify and categorize symptoms. The technique includes a model created from an automatic machine learning model selection tool. Tested on a hold-out set, its precision at the mention level was 0.80, recall 0.74 and an overall f-score of 0.80. The tool was scaled-up to process a large corpus of 964,105 patient records.

关键词： Natural Language Processing Machine Learning Diagnosis

作者: Guy Divita Gang Luo Le-Thuy T.Tran T.Elizabeth Workman Adi V.Gundlapalli Matthew H.Samore

作者单位: VA Salt Lake City Health Care System,Salt Lake City,Utah,USA;University of Utah School of Medicine,S Department of Biomedical Informatics and Medical Education,University of Washington,Seattle,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: 356-360

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）