Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics.

Autor:	Prasanna A; Booz Allen Hamilton., Jing B; Northern California Institute for Research and Education.; San Francisco VA Medical Center., Plopper G; Booz Allen Hamilton., Miller KK; Booz Allen Hamilton., Sanjak J; Booz Allen Hamilton., Feng A; Harvard University.; Sentieon Inc., Prezek S; Booz Allen Hamilton., Vidyaprakash E; Booz Allen Hamilton., Thovarai V; Booz Allen Hamilton., Maier EJ; Booz Allen Hamilton., Bhattacharya A; Booz Allen Hamilton., Naaman L; Booz Allen Hamilton., Stephens H; Currently Precigen., Watford S; Booz Allen Hamilton.; Currently U.S. Environmental Protection Agency., Boscardin WJ; University of California, San Francisco, Department of Medicine.; University of California, San Francisco, Department of Epidemiology & Biostatistics., Johanson E; U.S. Food and Drug Administration., Lienau A; VHA Innovation Ecosystem.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2023 Dec 13. Date of Electronic Publication: 2023 Dec 13.
DOI:	10.1101/2023.12.11.23298687
Abstrakt:	The COVID-19 pandemic had disproportionate effects on the Veteran population due to the increased prevalence of medical and environmental risk factors. Synthetic electronic health record (EHR) data can help meet the acute need for Veteran population-specific predictive modeling efforts by avoiding the strict barriers to access, currently present within Veteran Health Administration (VHA) datasets. The U.S. Food and Drug Administration (FDA) and the VHA launched the precisionFDA COVID-19 Risk Factor Modeling Challenge to develop COVID-19 diagnostic and prognostic models; identify Veteran population-specific risk factors; and test the usefulness of synthetic data as a substitute for real data. The use of synthetic data boosted challenge participation by providing a dataset that was accessible to all competitors. Models trained on synthetic data showed similar but systematically inflated model performance metrics to those trained on real data. The important risk factors identified in the synthetic data largely overlapped with those identified from the real data, and both sets of risk factors were validated in the literature. Tradeoffs exist between synthetic data generation approaches based on whether a real EHR dataset is required as input. Synthetic data generated directly from real EHR input will more closely align with the characteristics of the relevant cohort. This work shows that synthetic EHR data will have practical value to the Veterans' health research community for the foreseeable future.
Databáze:	MEDLINE
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