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GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health records and Continuous Monitoring Sensors

The R35 research program is focused on multicenter development and validation of sepsis predictive analytic algorithms. Drawing insights from recent advances in domain adaptation and multi-task learning (sub-fields of machine learning), this project aims to discover generalizable dynamic phenotypes that are directly relevant to the prediction and management of sepsis, septic shock, and downstream organ injury. Additionally, this program aims at advancing FHIR (Fast Healthcare Interoperability Resources) and OMOP (Observational Medical Outcomes Partnership) interoperability standards. The research program is conducted in close collaboration with our dissemination and implementation and hospital quality improvement teams to ensure early assessment of usability, barriers to implementation, and effective education to maximize the potential for clinical impact.

Principal Investigator (PI): Shamim Nemati

UCSD Team: Gabriel Wardi, Robert El-Kareh, Robert Ownes, Atul Malhotra, Nathan Young, Nicole Stadnick

Grant: NIH 5R35GM143121-03

Start Date: 08/01/2021

Expected Duration: 5 years

NIH Project Information