Predictive-analytic Models of Opioid Overdose and Reoffending (022)

The University of Chicago is developing open-source software that can be used by researchers and practitioners to predict overdose and re-offending risk of their population. This project will use large administrative datasets and machine-learning technology to develop a framework for transparent predictive models and simulations to help identify people at highest risk and how populations will benefit from interventions, and explore the likely policy impact of observed relationships among emerging trends to improve outcomes.

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