ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2025Review
- Prediction of QTc Prolongation in Acute Poisoning with Atypical Antipsychotics Using Machine Learning Techniques: A Study from Poison Control Center.Cardiovascular toxicology · 2025Article
- Population-level individualized prospective prediction of opioid overdose using machine learning.Molecular psychiatry · 2025Article
- Predicting Fatal Drug Poisoning Among People Living with HIV-HCV Co-Infection.Canadian liver journal · 2025Article
- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations.Current pain and headache reports · 2025Review
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Relative importance of socioecological domains to predicting opioid-involved mortality.PloS one · 2025Article
- Phenotyping people with a history of injecting drug use within electronic medical records using an interactive machine learning approach.NPJ digital medicine · 2024Article
- Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.Bioelectronic medicine · 2024Article
- Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.Annals of epidemiology · 2024Article
- Impact of 30-day prescribed opioid dose trajectory on fatal overdose risk: A population-based, statewide cohort study.Journal of general internal medicine · 2024Article
- A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.Frontiers in neurology · 2024Article
- HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.Plastic and reconstructive surgery · 2023Article
- Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling.Addiction (Abingdon, England) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This depends on critical knowledge to understand the risk of opioid overdose of patients. In this paper, we present our work on building machine learning based prediction models to predict opioid overdose of patients based on the history of patients' electronic health records (EHR). We performed two studies using New York State claims data (SPARCS) with 440,000 patients and Cerner's Health Facts database with 110,000 patients. Our experiments demonstrated that EHR based prediction can achieve best recall with random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), best precision with deep learning (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). We also discovered that clinical events are among critical features for the predictions.
Indexed as
Identifiers
32308832PMC7153049What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.