Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods
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RESEARCH
Open Access
Comparative analysis, applications, and interpretation of electronic health recordbased stroke phenotyping methods Phyllis M. Thangaraj1,2, Benjamin R. Kummer3, Tal Lorberbaum1,2, Mitchell S. V. Elkind4,5 and Nicholas P. Tatonetti1,2* * Correspondence: nick.tatonetti@ columbia.edu 1 Department of Biomedical Informatics, Columbia University, 622 W 168th St., PH-20, New York, NY 10032, USA 2 Department of Systems Biology, Columbia University, New York, NY, USA Full list of author information is available at the end of the article
Abstract Background: Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematically compared and evaluated the ability of machine learning algorithms and case-control combinations to phenotype acute ischemic stroke patients using data from an EHR. Materials and methods: Using structured patient data from the EHR at a tertiarycare hospital system, we built and evaluated machine learning models to identify patients with AIS based on 75 different case-control and classifier combinations. We then estimated the prevalence of AIS patients across the EHR. Finally, we externally validated the ability of the models to detect AIS patients without AIS diagnosis codes using the UK Biobank. Results: Across all models, we found that the mean AUROC for detecting AIS was 0.963 ± 0.0520 and average precision score 0.790 ± 0.196 with minimal feature processing. Classifiers trained with cases with AIS diagnosis codes and controls with no cerebrovascular disease codes had the best average F1 score (0.832 ± 0.0383). In the external validation, we found that the top probabilities from a model-predicted AIS cohort were significantly enriched for AIS patients without AIS diagnosis codes (60–150 fold over expected). Conclusions: Our findings support machine learning algorithms as a generalizable way to accurately identify AIS patients without using process-intensive manual feature curation. When a set of AIS patients is unavailable, diagnosis codes may be used to train classifier models. (Continued on next page)
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