Machine learning for phenotyping opioid overdose events.

ABSTRACT:

Objective

To develop machine learning models for classifying the severity of opioid overdose events from clinical data.

Materials and methods

Opioid overdoses were identified by diagnoses codes from the Marshfield Clinic population and assigned a severity score via chart review to form a gold standard set of labels. Three primary feature sets were constructed from disparate data sources surrounding each event and used to train machine learning models for phenotyping.

Results

Random forest and penalized logistic regression models gave the best performance with cross-validated mean areas under the ROC curves (AUCs) for all severity classes of 0.893 and 0.882 respectively. Features derived from a common data model outperformed features collected from disparate data sourc

SUBMITTER: Badger J 

PROVIDER: S-EPMC6622451 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature