Automated detection of substance use information from electronic health records for a pediatric population
| Autor: | Alycia Bachtel, Yizhao Ni, Sarah J. Beal, Katie Nause |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
AcademicSubjects/SCI01060
Adolescent Substance-Related Disorders Computer science automated substance use detection Health Informatics Health records Research and Applications computer.software_genre Machine Learning 03 medical and health sciences 0302 clinical medicine 030225 pediatrics Humans 030212 general & internal medicine natural language processing Child Rule of inference AcademicSubjects/MED00580 Narration business.industry Deep learning deep learning Unstructured data Predictive value electronic health records Artificial intelligence AcademicSubjects/SCI01530 Substance use business computer pediatric population Natural language processing Pediatric population |
| Zdroj: | Journal of the American Medical Informatics Association : JAMIA |
| ISSN: | 1527-974X |
| DOI: | 10.1093/jamia/ocab116 |
| Popis: | 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 structured EHR indicators to screening results. A knowledge-based NLP system and a deep learning model detected substance use information from unstructured clinical narratives. System performance was evaluated using positive predictive value, sensitivity, negative predictive value, specificity, and area under the receiver-operating characteristic curve (AUC). Results The dataset included 17 235 structured indicators and 27 141 clinical narratives. Manual review of clinical narratives captured 94.0% of positive screening results, while structured EHR data captured 22.0%. Logic rules detected screening results from structured data with 1.0 and 0.99 for sensitivity and specificity, respectively. The knowledge-based system detected substance use information from clinical narratives with 0.86, 0.79, and 0.88 for AUC, sensitivity, and specificity, respectively. The deep learning model further improved detection capacity, achieving 0.88, 0.81, and 0.85 for AUC, sensitivity, and specificity, respectively. Finally, integrating predictions from structured and unstructured data achieved high detection capacity across all cases (0.96, 0.85, and 0.87 for AUC, sensitivity, and specificity, respectively). Conclusions It is feasible to detect substance use screening and results among pediatric patients using logic rules, NLP, and machine learning technologies. |
| Databáze: | OpenAIRE |
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