Original research
by
Liu, Yang S. et al
Release Date
2022
Geography
Canada
Language of Resource
English
Full Text Available
Yes
Open Access / OK to Reproduce
No
Peer Reviewed
Yes
Objective
In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data.
Findings/Key points
With 6409 OUD cases in 2019, our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders.
Design/methods
Machine-learning model trained on a cross-linked Canadian administrative health data set
Keywords
About PWUD
Digital health
