A method to advance adolescent sexual health research: automated algorithm finds sexual history documentation
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Authors
Robertson, Caryn
Mukherjee, Gargi
Gooding, Holly
Kandaswamy, Swaminathan
Orenstein, Evan
Issue Date
2022-07-22
Type
Article
Language
en_US
Keywords
Adolescent , Natural Language Processing (Computer Science) , Sexual Health , Free Text , Sexually Transmitted Infections , Regular Expression (Regex)
Alternative Title
Abstract
Background:
We aimed to develop and validate a rule-based Natural Language Processing (NLP) algorithm to detect sexual history documentation and its five key components [partners, practices, past history of sexually transmitted infections (STIs), protection from STIs, and prevention of pregnancy] among adolescent encounters in the pediatric emergency and inpatient settings.
Methods
We iteratively designed a NLP algorithm using pediatric emergency department (ED) provider notes from adolescent ED visits with specific abdominal or genitourinary (GU) chief complaints. The algorithm is composed of regular expressions identifying commonly used phrases in sexual history documentation. We validated this algorithm with inpatient admission notes for adolescents. We calculated the sensitivity, specificity, negative predictive value, positive predictive value, and F1 score of the tool in each environment using manual chart review as the gold standard.
Results
In the ED test cohort with abdominal or GU complaints, 97/179 (54%) provider notes had a sexual history documented, and the NLP algorithm correctly classified each note. In the inpatient validation cohort, 97/321 (30%) admission notes included a sexual history, and the NLP algorithm had 100% sensitivity and 98.2% specificity. The algorithm demonstrated >97% sensitivity and specificity in both settings for detection of elements of a high quality sexual history including protection used and contraception. Type of sexual practice and STI testing offered were also detected with >97% sensitivity and specificity in the ED test cohort with slightly lower performance in the inpatient validation cohort.
Conclusion
This NLP algorithm automatically detects the presence of sexual history documentation and its key components in ED and inpatient settings.
Description
Citation
Robertson, C., Mukherjee, G., Gooding, H., Kandaswamy, S., & Orenstein, E. (2022). A method to advance adolescent sexual health research: Automated algorithm finds sexual history documentation. Frontiers in digital health, 4, 836733. https://doi.org/10.3389/fdgth.2022.836733
Publisher
Frontiers in Digital Health