# Identifying Personalized Risk of Acute Kidney Injury with Machine Learning

> **NIH NIH R01** · UNIVERSITY OF KANSAS MEDICAL CENTER · 2021 · $234,845

## Abstract

PROJECT SUMMARY/ABSTRACT
Acute Kidney Injury (AKI) is a common and highly lethal health problem, affecting 10-15% of all hospitalized
patients and >50% of patients in intensive care units (ICUs). It has been shown that a small increase in serum
creatinine (SCr) of ≥0.5 mg/dl was associated with a 6.5-fold increase in the odds of death, a 3.5-day increase
in length of stay, and nearly $7,500 in excess hospital costs. Unfortunately, no specific treatment exists to cure
AKI once it has developed. The ability to predict AKI in hospitalized patients would provide clinicians the
opportunity to modify care pathways and implement interventions, which could in turn prevent AKI and yield
better outcomes. Although electronic medical record (EMR) based monitoring systems for AKI have led to
expedited interventions and may increase the percentage of patients returning to baseline kidney function,
most of these systems are reactive rather than proactive, with little or no contribution to AKI prevention.
Moreover, our current knowledge of AKI risk factors is far from complete, especially in the ICU and general
inpatient populations, characterized by numerous deficiencies and systematic failings that may be avoidable
To transform the reactive AKI care to proactive and personalized care, early identification of high risk patients
and better understanding of individual modifiable risk factors for AKI is the key. In Aim 1, to discover novel risk
factors predictive of AKI, we propose to develop an ensemble multi-view feature selection framework to
simultaneously consider the differences and interrelations between feature spaces and obtain robust
knowledge by synthesizing findings from diverse patient populations across multiple institutions in nine US
states. In Aim 2, to discover general modifiable causes of AKI to help physicians design more effective AKI
prevention policies, we propose to develop a novel multi-cause inference method to identify causal
relationships between modifiable factors and AKI for susceptible patient subgroups. In Aim 3, to explain what
caused AKI in individual patients to support physicians in designing personalized AKI intervention, we propose
to develop a new causal explanation method by integrating causal inference and case based reasoning to
quantify patient-level causal significance of modifiable factors.
The proposed study will have a significant clinical impact by not only expanding the capacity of clinicians to
identify high risk patients for AKI early and advancing the general knowledge on causal and modifiable risk
factors for AKI but also supporting personalized AKI intervention with suggestions on potential patient-specific
actionable items. The work will not only advance AKI but also the machine learning and clinical research
informatics community and the methodology developed is generalizable to other clinical domains.

## Key facts

- **NIH application ID:** 10200790
- **Project number:** 5R01DK116986-03
- **Recipient organization:** UNIVERSITY OF KANSAS MEDICAL CENTER
- **Principal Investigator:** MEI LIU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $234,845
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200790

## Citation

> US National Institutes of Health, RePORTER application 10200790, Identifying Personalized Risk of Acute Kidney Injury with Machine Learning (5R01DK116986-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10200790. Licensed CC0.

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