Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets

Feature Importance An analysis of the feature importance of the AKI prediction model using SHAP values on the internal dataset showed that total surgery time and intraoperative blood pressure data were highly significant, whereas the gender status from the demographic data, and albumin and creatinine levels in the preoperative laboratory test data, played pivotal roles in the prediction performance ( Figure 3 ). 4.

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