Development and Validation of a Machine Learning Model to Estimate Risk of Adverse Outcomes Within 30 Days of Opioid Dispensation.

ABSTRACT:

Importance

Machine learning approaches can assist opioid stewardship by identifying high-risk opioid prescribing for potential interventions.

Objective

To develop a machine learning model for deployment that can estimate the risk of adverse outcomes within 30 days of an opioid dispensation as a potential component of prescription drug monitoring programs using access to real-world data.

Design, setting, and participants

This prognostic study used population-level administrative health data to construct a machine learning model. This study took place in Alberta, Canada (from January 1, 2018, to December 31, 2019), and included all patients 18 years and older who received at least 1 opioid dispensation from a community pharmacy within the province.

Exposures

Each

SUBMITTER: Sharma V 

PROVIDER: S-EPMC9857580 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature