ArticleBioData mining2019
Predicting opioid dependence from electronic health records with machine learning.
Article in BioData mining, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05253781 (Low Dose Aspirin for Preventing Intrauterine Growth Restriction and Preeclampsia in Sickle Cell Pregnancy), which is not on this map. Cited by 44 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Low Dose Aspirin for Preventing Intrauterine Growth Restriction and Preeclampsia in Sickle Cell Pregnancy (PIPSICKLE): a Randomized Controlled Trial
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 100 citations in OpenAlex.
- Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.Journal of addiction medicinePooled it
- Responsible AI for safer opioid risk management in older adults.npj health systems · 2026Article
- Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study.Journal of medical Internet research · 2026Article
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- Functional brain network identification in opioid use disorder using machine learning analysis of resting-state fMRI BOLD signals.Computers in biology and medicine · 2025Article
- Population-level individualized prospective prediction of opioid overdose using machine learning.Molecular psychiatry · 2025Article
- Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.Journal of substance use and addiction treatment · 2025Article
- Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.BMJ open quality · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 2025Article
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD).PLOS digital health · 2024Article
- Evaluation of an emergency department-based approach to reduce subsequent opioid overdoses.Journal of the American College of Emergency Physicians open · 2024Article
- Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework.JMIR AI · 2024Article
- A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.PloS one · 2024Article
- Article
- Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.Plastic and reconstructive surgery · 2023Article
- Process, structural, and outcome quality indicators to support perioperative opioid stewardship: a rapid review.Perioperative medicine (London, England) · 2023Article
- Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling.Addiction (Abingdon, England) · 2023Article
- A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.International journal of medical informatics · 2023Article
- Individualized Prospective Prediction of Opioid Use Disorder.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence.
resultsWe trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls.
conclusionsThe predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room.
Indexed as
Identifiers
What 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.