Deep Patient: Predict the Medical Future of Patients with Deep Learning and EHRs
The latest advances in deep learning provide new effective opportunities to model, represent, and learn from large amounts of heterogeneous medical data. Here, in this talk, we focus on applying deep learning to the electronic health records (EHRs). In particular, we review the data as well as the recent literature, we highlight limitations and needs for improved methods and applications, and we discuss the challenges to implement and deploy machine intelligence into the clinical domain. We then present Deep Patient, a general-purpose patient and phenotype representation derived from the EHRs that facilitates clinical predictive modeling and medical analysis, such as patient stratification and disease definition.
Riccardo Miotto is a senior data scientist in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai in New York and a member of the Institute for Next Generation Healthcare. Riccardo’s work encompasses the design of algorithms for information retrieval and machine learning applied to clinical data for personalized medicine. Previously, Riccardo worked on clinical trial search engines through free-text eligibility criteria processing and machine learning applied to music information retrieval, in the particular semantic discovery and recommendation, automatic tagging, and cover identification. Riccardo obtained his Ph.D. in Information Engineering from the University of Padova, Italy.