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In this paper we present a new method of uncovering patients with aortic valve diseases in large electronic health record systems through learning with multimodal data. The method automatically extracts clinically-relevant valvular disease features from five multimodal sources of information including structured diagnosis, echocardiogram reports, and echocardiogram imaging studies. It combines these partial evidence features in a random forests learning framework to predict patients likely to have the disease. Results of a retrospective clinical study from a 1000 patient dataset are presented that indicate that over 25 % new patients with moderate to severe aortic stenosis can be automatically discovered by our method that were previously missed from the records.

Identifying Patients at Risk for Aortic Stenosis Through Learning from Multimodal Data