Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury

逻辑回归 生命体征 机器学习 医学 算法 急性肾损伤 人工智能 肌酐 急诊医学 统计 数学 内科学 计算机科学 外科
作者
Joshua Parreco,Hahn Soe-Lin,Jonathan Parks,Saskya Byerly,Matthew Chatoor,Jessica L. Buicko,Nicholas Namias,Rishi Rattan
出处
期刊:American Surgeon [SAGE Publishing]
卷期号:85 (7): 725-729 被引量:35
标识
DOI:10.1177/000313481908500731
摘要

Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days for each value. Different machine learning classifiers (gradient boosted trees [GBT], logistic regression, and deep learning) were trained to predict AKI using the laboratory values, vital signs, and slopes. There were 151,098 ICU stays identified and the rate of AKI was 5.6 per cent. The best performing algorithm was GBT with an AUC of 0.834 ± 0.006 and an F-measure of 42.96 per cent ± 1.26 per cent. Logistic regression performed with an AUC of 0.827 ± 0.004 and an F-measure of 28.29 per cent ± 1.01 per cent. Deep learning performed with an AUC of 0.817 ± 0.005 and an F-measure of 42.89 per cent ± 0.91 per cent. The most important variable for GBT was the slope of the minimum creatinine (30.32%). This study identifies the best performing machine learning algorithms for predicting AKI using trends in laboratory values in ICU patients. Early identification of these patients using readily available data indicates that incorporating machine learning predictive models into electronic medical record systems is an inevitable requisite for improving patient outcomes.
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