Automated detection of substance use information from electronic health records for a pediatric population.

ABSTRACT:

Objective

Substance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data.

Materials and methods

Pediatric patients (10-20 years of age) with any encounter between July 1, 2012, and October 31, 2017, were included (n = 3890 patients; 19 478 encounters). EHR data were extracted at each encounter, manually reviewed for substance use (alcohol, tobacco, marijuana, opiate, any use), and coded as lifetime use, current use, or family use. Logic rules mapped structu

SUBMITTER: Ni Y 

PROVIDER: S-EPMC8449626 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature