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

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-09-29 DOI:10.1002/mp.70038

Shengwen Guo, Yuanhan Chen, Yu Kuang, Qin Zhang, Yanhua Wu, Zhen Xie, Ziqiang Chen, Qiang He, Feng Ding, Guohui Liu, Yuanjiang Liao, Chen Lu, Li Hao, Jing Sun, Lang Zhou, Rui Fang, Qingquan Luo, Haiquan Huang, Qi Cheng, Xinling Liang

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引用次数: 0

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 predicting AKI, even without SCr data.

Methods

This multicenter observational study, conducted from 2010 to 2016 across 15 hospitals in China, employed the Light Gradient Boosting Machine (LightGBM) to create predictive models. The model's performance was assessed using area under the curve (AUC), precision, recall, specificity, and accuracy.

Results

A total of 561 137 hospitalized patients were eligible for the analyses, of whom 45 610 were diagnosed with AKI. The LightGBM model demonstrated high accuracy in predicting AKI, with AUC values ranging from 0.860 to 0.986. The study showed that non-standard EMRs could effectively predict AKI. Importantly, the model maintained strong predictive performance even without SCr data, indicating that AKI can be accurately predicted without this traditional biomarker.

Conclusion

Non-standard EMRs are valuable for predicting AKI, even in the absence of SCr data. This approach is particularly useful in resource-limited settings, where traditional biomarkers are often unavailable, demonstrating the potential of other clinical features to compensate for missing SCr data in AKI prediction.

Abstract Image

查看原文本刊更多论文

在资源有限的情况下，使用非标准电子病历开发机器学习驱动的急性肾损伤预测模型。

背景：急性肾损伤（AKI）仍然是一个重大的全球健康挑战，特别是在资源有限的环境中。大多数现有的预测模型严重依赖于血清肌酐（SCr）水平和标准化电子医疗记录（emr）。然而，在许多低资源环境中，SCr测试很少，并且EMR系统通常在数据结构、术语和记录实践（即非标准EMR）方面缺乏标准化。这些限制阻碍了准确预测AKI所需特征的一致提取，并突出了迫切需要针对多样化和资源有限的医疗保健环境量身定制自适应框架。目的：本研究旨在开发和验证使用非标准化emr预测AKI的机器学习模型，即使没有SCr数据。方法：本多中心观察研究于2010年至2016年在中国15家医院开展，采用光梯度增强机（LightGBM）建立预测模型。使用曲线下面积（AUC）、精密度、召回率、特异性和准确性来评估模型的性能。结果：共有561 137例住院患者符合分析条件，其中45 610例被诊断为AKI。LightGBM模型预测AKI的准确率较高，AUC值在0.860 ~ 0.986之间。研究表明，非标准emr可以有效预测AKI。重要的是，即使没有SCr数据，该模型也保持了很强的预测性能，这表明在没有这种传统生物标志物的情况下，AKI可以准确预测。结论：即使在没有SCr数据的情况下，非标准emr对预测AKI也是有价值的。这种方法在资源有限的情况下特别有用，在这些情况下，传统的生物标志物通常是不可用的，这表明了其他临床特征在AKI预测中弥补SCr数据缺失的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.