aceImminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the <i>All of Us</i> research program
Background: Identifying patients at risk of opioid overdose in healthcare settings is critical, yet evidence on predictive models and their performance to predict imminent opioid overdose remains limited. Objective: We compared classical and Machine Learning (ML) survival models to predict 30-day overdose risk following a first opioid-related diagnosis to determine whether algorithmic complexity improves clinical decision support. Methods: We conducted a prospective cohort study using longitudinal Electronic Hea…
Survival models including machine learning approaches predicted imminent 30-day opioid overdose following a first opioid-related diagnosis with C-indices up to 0.745, identifying prior overdose and opioid misuse as strongest predictors for proactive clinical intervention.
Evidence
- Peer-reviewedThe American Journal of Drug and Alcohol Abuse2026-07-30
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Truvace Impact Record TRV-2026-0603, v1: “Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the <i>All of Us</i> research program.” Truvace, 2026-07-31. /record/TRV-2026-0603 (accessed at citation time). sha256 056ae17939d0e3d6…
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