Developing machine learning-driven acute kidney injury predictive models using non-standard EMRs in resource-limited settings.

ABSTRACT:

Background

Acute Kidney Injury (AKI) remains a significant global health challenge, especially in resource-limited settings. Most existing predictive models rely heavily on serum creatinine (SCr) levels and standardized electronic medical records (EMRs). However, in many low-resource environments, SCr testing is infrequent, and EMR systems often lack standardization in data structure, terminology, and recording practices (a.k.a., non-standard EMRs). These limitations hinder the consistent extraction of features needed for accurate AKI prediction and highlight the urgent need for adaptive frameworks tailored to diverse and resource-limited healthcare environments.

Purpose

This study aimed to develop and validate a machine learning model using non-standardized EMRs for predict

SUBMITTER: Guo S 

PROVIDER: S-EPMC12480061 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature