Identifying Personalized Risk of Acute Kidney Injury with Machine Learning
PI(s): Mei Liu, Kansas University Medical Center
PaTH Site PI(s): John Kellum
Project Summary: This project aims to address three critical clinical questions:
- Who are the high-risk patients for developing acute kidney injury (AKI) in the hospital?
- What are the modifiable risk factors of AKI to avoid for the high-risk population?
- What are the specific risk factors of AKI to avoid for an individual patient?
In order to answer these questions, we are using electronic health records of diverse populations from multiple institutions across the US with novel machine learning techniques.
Study Design: Multi-CRN retrospective observational cohort study
PCORnet Partners: Greater Plains Collaborative (GPC)
PaTH Partners: UUniversity of Pittsburgh (John Kellum, Site PI)
Sponsor: NIH / NIDDK
