Original research
par
Liu, Yang S. et al
Date de publication
2022
Géographie
Canada
Langue de la ressource
English
Texte disponible en version intégrale
Oui
Open Access / OK to Reproduce
Non
Évalué par des pairs
Yes
L’objectif
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.
Constatations/points à retenir
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.
La conception ou méthodologie de recherche
Machine-learning model trained on a cross-linked Canadian administrative health data set
Mots clés
About PWUD
Digital health
