Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment.
Journal:
Journal of acquired immune deficiency syndromes (1999)
Published Date:
Feb 1, 2018
Abstract
OBJECTIVE: 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.
Authors
Keywords
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Automation
Child
Child, Preschool
Cohort Studies
Decision Support Techniques
Electronic Health Records
Female
HIV Infections
Humans
Infant
Infant, Newborn
Male
Mass Screening
Middle Aged
Natural Language Processing
New York City
Risk Assessment
Risk Factors
Young Adult