Observational studyMedical physics2025
Developing machine learning-driven acute kidney injury predictive models using non-standard EMRs in resource-limited settings.
Observational study in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence-guided nutritional therapy in the ICU.Current opinion in clinical nutrition and metabolic care · 2026Review
- Developing machine learning-driven acute kidney injury predictive models using non-standard EMRs in resource-limited settings.Medical physics · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
backgroundAcute 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.
methodsThis 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.
resultsA 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.
conclusionNon-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.