Are machine learning models built on healthcare data reliable at predicting opioid use disorder? That’s what researchers from Florida Atlantic University’s College of Engineering and Computer Science wanted to explore. As such, they examined peer-reviewed journal papers and conducted one of the first systematic reviews analyzing not only the technical aspects of machine learning applied to predicting opioid use, but also the published results.
Their goal was to determine if these machine learning methods are useful and, more importantly, reproducible. For the study, they reviewed 16 peer-reviewed journal papers that used machine learning models to predict opioid use disorder and investigated how the papers trained and evaluated these models.
Findings, published in the journal Computer Methods and Programs in Biomedicine, reveal that while results from the reviewed papers show machine learning models applied to opioid use disorder prediction may be useful, there are important ways to improve transparency and reproducibility of these models, which will ultimately enhance their use for research.
