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UBC Theses and Dissertations

A machine learning approach to overdose risk assessment Tai, Man Yeung (Andy)


This project explores the escalating drug toxicity crisis in British Columbia, spotlighting the role of machine learning within eHealth solutions to combat this pressing public health issue. The crisis, rooted in a shift from prescription opioids to potent synthetic alternatives like fentanyl, necessitates innovative intervention strategies. The study leverages extensive data from 2015 to 2019, aiming to develop predictive models for overdoses to improve healthcare responses. The research begins with a systematic review and meta-analysis of machine learning models targeting opioid-related outcomes, demonstrating the predictive strengths of various algorithms in cohort studies. Results indicate these algorithms' effectiveness in forecasting opioid usage and overdose risks. Additionally, a review of clinical decision support systems in addiction and mental health care reveals their critical impact on enhancing diagnosis, treatment, and patient care, based on randomized controlled trials. Central to the thesis is an exploratory data analysis utilizing the British Columbia Provincial Overdose Cohort. This involves rigorous data preparation, including wrangling, addressing missing data, and correcting class imbalances. The study assesses several machine learning models, including ensemble approaches like Random Forest and XGBoost, for their predictive accuracy regarding fatal and general overdoses. Despite challenges, these models demonstrate significant potential, with the best performers achieving over 90% accuracy in predicting general overdoses, though models for fatal overdoses showed lower efficacy. The thesis concludes with an affirmation of machine learning's transformative potential in personalizing addiction psychiatry treatment through eHealth innovations such as the Risk Assessment and Management Platform (RAMP). This novel approach aims to individualize treatment and prevention strategies, contributing to global efforts in mitigating the mental health ramifications of the drug toxicity crisis and transforming healthcare practices. Through the strategic implementation of machine learning, the study underscores a promising avenue for advancing healthcare solutions tailored to the intricacies of addiction and overdose risks, reflecting a significant stride towards mitigating the public health impacts of the drug crisis.

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