Identifying Personalized Risk of Acute Kidney Injury with Machine Learning
PI(s): Mei Liu, PhD
PaTH Site PI(s): John Kellum
Purpose:This project aims to address three critical clinical questions: (1) Who are the high-risk patients for developing acute kidney injury (AKI) in the hospital? (2) What are the modifiable risk factors of AKI to avoid for the high-risk population? (3) What are the specific risk factors of AKI to avoid for an individual patient?
Goal(s): To identify clinical risk factors of acute kidney injury (AKI) in hospitalized patients from electronic medical records of diverse populations from multiple institutions across the US with novel machine learning techniques.
Study Design: Retrospective observational cohort study
PCORnet Partners: Greater Plains Collaborative (GPC)
PaTH Partners: University of Pittsburgh