A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Tewodros Eguale François Bastardot Wenyu Song Daniel Motta-Calderon Yasmin Elsobky Angela Rui Marlika Marceau Clark Davis Sandya Ganesan Ava Alsubai Michele L Matthews Lynn A Volk David Westfall Bates Ronen Rozenblum

Published in: JMIR medical informatics (2024)

A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

Keyphrases

machine learning
end stage renal disease
ejection fraction
newly diagnosed
chronic kidney disease
palliative care
chronic pain
artificial intelligence
peritoneal dialysis
deep learning
patient reported outcomes
patient reported