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Open AccessArticle

Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
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J. Clin. Med. 2018, 7(10), 322; https://doi.org/10.3390/jcm7100322
Received: 9 September 2018 / Revised: 1 October 2018 / Accepted: 2 October 2018 / Published: 3 October 2018
Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data were obtained. The primary outcome was postoperative acute kidney injury (AKI) defined according to the Kidney Disease Improving Global Outcomes criteria. The following machine learning techniques were used: decision tree, random forest, extreme gradient boosting, support vector machine, neural network classifier, and deep learning. The performance of these techniques was compared with that of logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUC). During the first postoperative week, AKI occurred in 770 patients (38.3%). The best performance regarding AUC was achieved by the gradient boosting machine to predict the AKI of all stages (0.78, 95% confidence interval (CI) 0.75–0.80) or stage 2 or 3 AKI. The AUC of logistic regression analysis was 0.69 (95% CI 0.66–0.72). Decision tree, random forest, and support vector machine showed similar performance to logistic regression. In our comprehensive comparison of machine learning approaches with logistic regression analysis, gradient boosting technique showed the best performance with the highest AUC and lower error rate. We developed an Internet–based risk estimator which could be used for real-time processing of patient data to estimate the risk of AKI at the end of surgery. View Full-Text
Keywords: acute kidney injury; cardiovascular surgery; machine learning acute kidney injury; cardiovascular surgery; machine learning
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MDPI and ACS Style

Lee, H.-C.; Yoon, H.-K.; Nam, K.; Cho, Y.J.; Kim, T.K.; Kim, W.H.; Bahk, J.-H. Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery. J. Clin. Med. 2018, 7, 322.

AMA Style

Lee H-C, Yoon H-K, Nam K, Cho YJ, Kim TK, Kim WH, Bahk J-H. Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery. Journal of Clinical Medicine. 2018; 7(10):322.

Chicago/Turabian Style

Lee, Hyung-Chul; Yoon, Hyun-Kyu; Nam, Karam; Cho, Youn J.; Kim, Tae K.; Kim, Won H.; Bahk, Jae-Hyon. 2018. "Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery" J. Clin. Med. 7, no. 10: 322.

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