ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2022
Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.Biological psychiatry · 2025Article
- Telemedicine-Supported Exercise Interventions in Hematopoietic Stem Cell Transplantation: A Scoping Review.International journal of general medicine · 2025Review
- Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.JMIR medical informatics · 2024Article
- Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach.JMIR mental health · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Potential of natural language processing (NLP) in extracting patient's information from clinical notes of opioid treatment programs (OTP) and leveraging it in development of predictive models has not been fully explored. The goal of this study was to assess potential of NLP in identifying legal, social, mental, medical and family environment-based determinants of distress from clinical narratives of patients with opioid addiction, and then using this information in predicting OTP outcomes. Around 63% of patients reported improvements after completing OTP. We compared the results of logistics regression and random forest for predictive modeling. Random forest model performed slightly better than logistic regression (75% F1 score) with 74% accuracy. Clinical Relevance- Psychiatric and medical disorders, social, legal and family-based distress are important determinants of distress in patients enrolled in OTP. These information are often recorded in clinical notes. Extraction of this information and their utilization as features in machine learning models will lead to the enhancement of the performance of the OTP outcome predictive models.
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Registered trials
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