{"title":"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。","authors":"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","doi":"10.1002/mp.70038","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>This study aimed to develop and validate a machine learning model using non-standardized EMRs for predicting AKI, even without SCr data.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12480061/pdf/","citationCount":"0","resultStr":"{\"title\":\"Developing machine learning-driven acute kidney injury predictive models using non-standard EMRs in resource-limited settings\",\"authors\":\"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\",\"doi\":\"10.1002/mp.70038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>This study aimed to develop and validate a machine learning model using non-standardized EMRs for predicting AKI, even without SCr data.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>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.</p>\\n </section>\\n </div>\",\"PeriodicalId\":18384,\"journal\":{\"name\":\"Medical physics\",\"volume\":\"52 10\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12480061/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70038\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"3","ListUrlMain":"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70038","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Developing machine learning-driven acute kidney injury predictive models using non-standard EMRs in resource-limited settings
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.
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.
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