Machine learning for phenotyping opioid overdose events.


Journal Article

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 sources for the same cohort of patients (AUCs 0.893 versus 0.837, p value = 0.002). The addition of features extracted from free text to machine learning models also increased AUCs from 0.827 to 0.893 (p value < 0.0001). Key word features extracted using natural language processing (NLP) such as 'Narcan' and 'Endotracheal Tube' are important for classifying overdose event severity. CONCLUSION: Random forest models using features derived from a common data model and free text can be effective for classifying opioid overdose events.

Full Text

Duke Authors

Cited Authors

  • Badger, J; LaRose, E; Mayer, J; Bashiri, F; Page, D; Peissig, P

Published Date

  • June 2019

Published In

Volume / Issue

  • 94 /

Start / End Page

  • 103185 -

PubMed ID

  • 31028874

Pubmed Central ID

  • 31028874

Electronic International Standard Serial Number (EISSN)

  • 1532-0480

Digital Object Identifier (DOI)

  • 10.1016/j.jbi.2019.103185


  • eng

Conference Location

  • United States