عنوان انگلیسی مقاله:
Machine learning for phenotyping opioid overdose events
ترجمه فارسی عنوان مقاله:
یادگیری ماشین برای فنوتیپ وقایع مصرف بیش از حد مواد افیونی
Sciencedirect - Elsevier - Journal of Biomedical Informatics, 94 (2019) 103185: doi:10:1016/j:jbi:2019:103185
Jonathan Badgera,c,⁎, Eric LaRosea, John Mayera, Fereshteh Bashiria, David Pageb,c, Peggy Peissiga
Objective: To develop machine learning models for classifying the severity of opioid overdose events from
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.
Keywords: Machine learning | Opioid | Phenotype | Overdose | Electronic health record