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Enhanced Metadata Design, Architecture, and Learning (MeDAL) for Development of Generalizable Deep Learning-based Predictive Analytics from Electronic Health Records

Project Description

Sepsis, Septic Shock, Acute Kidney Injury (AKI), acute respiratory distress syndrome (ARDS) and respiratory failure are among the top causes of hospital mortality, morbidity, and an increase in duration and cost of hospitalization. Successful prevention and management of these conditions rely on the ability of clinicians to estimate the risk, and ideally, to anticipate and prevent these events. The purpose of this R01 project is to design new deep learning architectures that are robust to data missingness and biases introduced through the variability in process of care, 2) development of new learning methodologies to improve generalizability of the proposed models under data/population drifts (aka distributional changes), 3) enhanced metadata design to assist in quantifying `conditions for use' of such algorithms via algorithmic controls, and 4) HL7 and FHIR-based prospective implementation and testing of these methodologies to provide real-world clinical evidence for the effectiveness of the proposed approaches.

Principal Investigator (PI): Shamim Nemati

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

Grant: NIH: 1R01LM013998-01

Start Date: 5/01/2022

Expected End Date: 01/31/2026

NIH Project Information