Extracting Sexual Trauma Mentions from Electronic Medical Notes Using Natural Language Processing
Patient history of sexual trauma is of clinical relevance to healthcare providers as survivors face adverse health-related outcomes. This paper describes a method for identifying mentions of sexual trauma within the free text of electronic medical notes. A natural language processing pipeline for information extraction was developed and scaled to handle a large corpus of electronic medical notes used for this study from US Veterans Health Administration medical facilities. The tool was used to identify sexual trauma mentions and create snippets around every asserted mention based on a domain-specific lexicon developed for this purpose. All snippets were evaluated by trained human reviewers. An overall positive predictive value (PPV) of 0.90 for identifying sexual trauma mentions from the free text and a PPV of 0.71 at the patient level are reported. The metrics are superior for records from female patients.
Natural Language Processing Information Retrieval Trauma and Stressor Related Disorders
Guy Divita Emily Brignone Marjorie E.Carter Ying Suo Rebecca K.Blais Matthew H.Samore Jamison D.Fargo Adi V.Gundlapalli
VA Salt Lake City Health Care System,Salt Lake City,Utah,USA;University of Utah School of Medicine,S VA Salt Lake City Health Care System,Salt Lake City,Utah,USA;Utah State University Department of Psy
国际会议
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)
苏州
英文
351-355
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）