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Category: Project

Description: Despite widespread adoption of Electronic Health Records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for patient prognosis. Using this framework, currently comprised of thousands of cancer patients and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof-of-concept we report an analysis using this infrastructure which identified the Framingham risk score to be robustly associated with mortality among both early-stage and advanced- stage cancer patients, a potentially actionable finding from a real-world cohort of oncology patients. Finally, we show how natural language processing of medical notes could be used to continuously update estimates of prognosis as a given patient’s disease course unfolds.