# Identifying Personalized Risk of Acute Kidney Injury with Machine Learning

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2021 · $271,307

## Abstract

PROJECT SUMMARY
Acute Kidney Injury (AKI) is a heterogeneous syndrome that has multiple etiologies, variable pathogenesis,
and diverse outcomes. For example, congestive heart failure and dehydration can produce identical changes in
serum creatinine level and urine output (i.e. parameters used to define AKI); however, they differ vastly in their
physiological contexts and demand completely opposite treatments. AKI is common in hospitalized patients,
affecting 10% to 15% inpatients in the general units and >50% in the intensive care units. Regardless of the
underlying cause, even mild forms of AKI are associated with 6.5-fold increase in mortality.
 The current clinical management guideline for AKI is based on minimizing the risk of developing AKI and
providing supportive care. However, the sheer number of known and potentially unknown risk factors of AKI
and the complex interactions among them make it impossible for physicians to analyze and forecast AKI risk
for a single patient in real time. Machine learning has demonstrated its success in modeling complex electronic
health record (EHR) data for disease risk predictions, including AKI. Overwhelming majority of the clinical risk
prediction models are trained on data from a predefined patient cohort, also known as a global prediction
model, optimized for the supposedly “average” patient. However, the one-size-fits-all prediction model may not
work for all patients. Findings from our work in current funding cycle revealed that a global model can make
completely wrong predictions for patients in high-risk and heterogeneous (variable pathogenesis) subgroups
because it only captures knowledge generalizable to a study population but miss subtle risk drivers specific to
an individual patient.
 Personalized modeling is a promising approach in which a model is trained on-demand for each patient by
identifying an individualized retrospective cohort of similar patients. In the current funding cycle, we developed
and validated a novel personalized AKI prediction framework and demonstrated its ability to capture patient
heterogeneity with an improved AKI risk prediction for individuals. Building on the success of our current
project, in this renewal application, we propose to focus the development of new machine learning methods
within the personalized modeling framework to gain deeper understanding of the personalized AKI risk and its
predictors from individualized cohorts. We designed three specific aims within the personalized modeling
framework to answer three important clinical questions: (1) what is an individual patient’s risk for developing
AKI during hospital stay? (2) why is an individual patient at risk for developing AKI during hospital stay? (3)
when will an individual patient’s kidney function recover after AKI onset? The proposed research has the
potential to advance personalized decision support, inform personalized intervention, and facilitate shared
decision making for providers, AKI patient...

## Key facts

- **NIH application ID:** 10992250
- **Project number:** 7R01DK116986-04
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** MEI LIU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $271,307
- **Award type:** 7
- **Project period:** 2023-12-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10992250

## Citation

> US National Institutes of Health, RePORTER application 10992250, Identifying Personalized Risk of Acute Kidney Injury with Machine Learning (7R01DK116986-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10992250. Licensed CC0.

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