Open AccessUsing Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment
Author(s) -
Daniel Feller,
Jason Zucker,
Michael T. Yin,
Peter Gordon,
Noémie Elhadad
Publication year - 2018
Publication title -
journal of acquired immune deficiency syndromes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.162
H-Index - 157
eISSN - 1944-7884
pISSN - 1525-4135
DOI - 10.1097/qai.0000000000001580
Subject(s) - baseline (sea) , artificial intelligence , machine learning , medicine , leverage (statistics) , natural language processing , propensity score matching , computer science , oceanography , geology
Universal HIV screening programs are costly, labor intensive, and often fail to identify high-risk individuals. Automated risk assessment methods that leverage longitudinal electronic health records (EHRs) could catalyze targeted screening programs. Although social and behavioral determinants of health are typically captured in narrative documentation, previous analyses have considered only structured EHR fields. We examined whether natural language processing (NLP) would improve predictive models of HIV diagnosis.