ArticleApplied clinical informatics2024
Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.
Article in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions.World journal of methodology · 2026Review
- TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record trajectories.BioData mining · 2026Article
- Perioperative Wearable Device Features are associated with Moderate-to-Severe Chronic Pain after Surgery in the "All of Us" Research Program.medRxiv : the preprint server for health sciences · 2025Article
- The Role of Pharmacogenetic Biomarkers in Pain.Biomedicines · 2025Article
- Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Automating Responses to Patient Portal Messages Using Generative AI.Applied clinical informatics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundManaging acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.
objectivesThis study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.
methodsThe study cohort consisted of approximately 347
resultsThe stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes.
conclusionSHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.
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Registered trials
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