General Symptom Extraction from VA Electronic Medical Notes

• Published in: Studies in health technology and informatics Vol. 245; p. 356
• Main Authors: Divita, Guy, Luo, Gang, Tran, Le-Thuy T, Workman, T Elizabeth, Gundlapalli, Adi V, Samore, Matthew H
• Format: Journal Article
• Language: English
• Published: Netherlands 2017
• Subjects: Data Mining; Electronic Health Records; Humans; Machine Learning; Natural Language Processing; Machine Learning; Natural Language Processing; Diagnosis
• ISSN: 0926-9630
• DOI: 10.3233/978-1-61499-830-3-356

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.