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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedZhang J, Kummerfield E, Hultman G
Application of causal discovery algorithms in studying the nephrotoxicity of remdesivir using longitudinal data from the EHR.
Researchers analyzed the role of remdesivir in the mechanism and optimal treatment of the development of acute kidney injury (AKI) in the setting of COVID. Applying causal discovery machine learning techniques, they built multifactorial causal models of COVID-AKI; risk factors and renal function measures were represented in a temporal sequence using longitudinal data from Electronic Health Records. Their results indicated a need for assessment of renal function on second- and third-day use of remdesivir, and also showed that remdesivir may pose less risk to AKI than existing conditions of chronic kidney disease.
AHRQ-funded; HS024532.
Citation: Zhang J, Kummerfield E, Hultman G .
Application of causal discovery algorithms in studying the nephrotoxicity of remdesivir using longitudinal data from the EHR.
AMIA Annu Symp Proc 2023 Apr 29; 2022:1227-36..
Keywords: COVID-19, Electronic Health Records (EHRs), Health Information Technology (HIT), Medication, Adverse Drug Events (ADE), Adverse Events