There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.
This talk focuses on advances in deep learning applied to precision medicine and, especially, on "deep patient", a general-purpose patient representation derived from the electronic health records (EHRs) that facilitates clinical predictive modeling. Precision medicine raises big challenges in dealing with large and massive data from heterogeneous sources, such as EHRs, genomics, and wearables. Deep learning provides a unique opportunity to retrieve information from these complex and heterogeneous sources. Here, in particular, we show how a deep architecture was able to process aggregated EHRs from the Mount Sinai Health System data warehouse to derive domain-free patient representations that can improve automatic medical predictions given the patient clinical status.