Predicting opioid overdose risk using machine learning: Evaluation of an AI approach

Bo (Cloud) Cao & Giri Puligandla

This project’s aim was to address the opioid crisis by enhancing an existing AI/machine learning model that uses large-scale population health data to identify risk factors and predict opioid overdose (OpOD). Throughout the process, researchers collaborated closely with communities and stakeholders to gain insights on how these predictions could be applied meaningfully in real-world settings.

Methodology

The first component of the study aimed to develop and validate a population-level individualized prospective prediction model of opioid overdose (OpOD) using machine learning and de-identified provincial administrative health data. The OpOD prediction model was based on a cohort of approximately 4 million people in 2017 to predict OpOD cases in 2018 and was subsequently tested on cohort data from 2018, 2019, and 2020 to predict OpOD cases in 2019, 2020, and 2021, respectively, and then update and validate the model longitudinally.

The second component of the study aimed to engage patients, people at risk and their families, as well as clinicians, emergency services, and policy makers to evaluate the potential benefits and risks of such prediction models and channels of real-world implementation.

Findings

• The model’s predictive performance, including balanced accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristics Curve (AUC), was evaluated, achieving a balanced accuracy of 83.7, 81.6, and 85.0% in each respective year.

• The leading predictors for OpOD, which were derived from health care utilization variables documented by the Canadian Institute for Health Information (CIHI) and physician billing claims, were treatment encounters for drug or alcohol use, depression, neurotic/anxiety/obsessive-compulsive disorder, and superficial skin injury.

• The main contribution of this study was to demonstrate that individualized OpOD prediction using existing population-level data can provide accurate prediction of future OpOD cases in the whole population and may have the potential to inform targeted interventions and policy planning.

Project Outreach

The project is currently exclusively based in Alberta but may expand nationally once the model receives clinical validation. It actively involves diverse individuals with lived experience in the development and community implementation of the technology, paving the way for future scaling across Canada.

Collaborations with healthcare teams and community organizations, including the Canadian Mental Health Association (CMHA), are underway to test real-world applications.

Resources Created

• 3 publications:

  • https://www.nature.com/articles/s41380-025-02992-4.epdf

  • https://journals.plos.org/mentalhealth/article?id=10.1371/journal.pmen.0000319

  • https://www.openaccessgovernment.org/article/computational-psychiatry-and-the-opioid-crisis-a-deep-dive/187134/

• Media article discussing findings about opioid use disorder:

  • https://bbrfoundation.org/content/machine-learning-harnessed-predict-risk-opioid-use-disorder

• Featuring on a media article:

  • https://www.ualberta.ca/en/medicine/news/2025/06/u-of-a-researchers-use-machine-learning-to-predict-future-opioid-overdose-cases.html

• Public talk at the University of Alberta Addiction Medicine Research Committee.

• An event organized by CMHA Edmonton for public knowledge sharing.

• YouTube Video further discussing this research project: