Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods.

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.