151. AKI Prediction Models in ICU: A Comparative Study (Preprint)
- Author
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Qing Qian, Haixia Sun, Jinming Wu, Jiayang Wang, and Lei Yang
- Abstract
BACKGROUND Acute kidney injury (AKI) is highly prevalent in critically ill patients and associated with significant morbidity and mortality as well as high financial costs. Early prediction of AKI provides an opportunity to develop strategies for early diagnosis, effective prevention, and timely treatment. Machine learning models have been developed for early prediction of AKI on critically ill patients by different researchers under different scenario. OBJECTIVE This comparative study aims to assess the performances of existing models for early prediction of AKI in the Intensive Care Unit (ICU) setting. METHODS The data was collected from the MIMIC-III database for all patients above 18 years old who had valid creatinine measured for 72 hours following ICU admission. Those with existing condition of kidney disease on admission were excluded. 17 predictor variables including patient demographics and physiological indicators were selected on the basis of the Kidney Disease Improving Global Outcomes (KDIGO) and medical literatures. Six models from three different types of methods were tested including Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Decision (LightGBM), and Convolutional Neural Network (CNN). The area under ROC curve (AUC), accuracy, precision, recall and F1 value were calculated for each model to evaluate the performance. RESULTS We extracted 17205patient ICU records from MIMIC-III dataset. LightGBM had the best performance, with all the evaluation indicators achieved the highest (with average AUC 0.905, F1 0.897, Recall 0.836, P CONCLUSIONS LightGBM demonstrated the best predictive capability in predicting AKI present at the first 72 hours of ICU admission. LightGBM and XGBoost showed great potential for clinical application owing to their high recall. This study can provide references for AI-powered clinical decision support system for early AKI prediction in ICU setting.
- Published
- 2020
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