Aghamirzaei Faezeh, Abin Ahmad Ali, Futuhi Farzaneh
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
Department of Nephrology, Shahid Beheshti University of Medical Science, Tehran, Iran.
Arch Acad Emerg Med. 2025 Apr 15;13(1):e45. doi: 10.22037/aaemj.v13i1.2560. eCollection 2025.
Acute Kidney Injury (AKI) is a severe complication of vancomycin treatment due to its nephrotoxic effects. However, research on predicting AKI in this high-risk group remains limited. This study presents a stacking ensemble machine learning model designed to predict the onset of AKI in this patient population.
Leveraging data from 314 ICU patients, the model incorporates SHapley Additive exPlanations (SHAP) for enhanced interpretability, identifying key predictors such as serum creatinine levels, glucose variability, and patient age. The model achieved an Area Under the Curve (AUC) of 0.94, outperforming existing predictive approaches. By utilizing readily available clinical data and determining an optimal temporal prediction window, this model facilitates proactive clinical decision-making, aiming to reduce the risk of AKI and improve patient outcomes.
The stacking ensemble model achieved 92% accuracy, 93% precision, 92% sensitivity, and 0.94 AUC in 314 ICU patients, pinpointing creatinine, glucose variability, and age as critical AKI predictors.
The findings suggest that integrating advanced machine learning techniques with interpretable artificial intelligence (AI) can provide a scalable and cost-effective solution for early AKI detection in diverse healthcare settings.
急性肾损伤（AKI）是万古霉素治疗的一种严重并发症，因其具有肾毒性作用。然而，针对这一高风险群体预测AKI的研究仍然有限。本研究提出了一种堆叠集成机器学习模型，旨在预测该患者群体中AKI的发病情况。
该模型利用314名重症监护病房（ICU）患者的数据，纳入了SHapley加性解释（SHAP）以增强可解释性，识别血清肌酐水平、血糖变异性和患者年龄等关键预测因素。该模型的曲线下面积（AUC）达到0.94，优于现有的预测方法。通过利用易于获得的临床数据并确定最佳时间预测窗口，该模型有助于进行积极的临床决策，旨在降低AKI风险并改善患者预后。
该堆叠集成模型在314名ICU患者中实现了92%的准确率、93%的精确率、92%的灵敏度和0.94的AUC，确定肌酐、血糖变异性和年龄为AKI的关键预测因素。
研究结果表明，将先进的机器学习技术与可解释的人工智能（AI）相结合，可以为不同医疗环境中早期检测AKI提供一种可扩展且具有成本效益的解决方案。
