Additional file 1 of Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence
Creators: name: Xu, Lingyu
name: Li, Chenyu
name: Zhang, Jiaqi
name: Guan, Chen
name: Zhao, Long
name: Shen, Xuefei
name: Zhang, Ningxin
name: Li, Tianyang
name: Yang, Chengyu
name: Zhou, Bin
name: Bu, Quandong
name: Xu, Yan
Publisher: name: figshare
Year: 2024
Subjects: Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Science Policy

description: Additional file 1. Fig. S1. ROC curves and AUROC values for mortality prediction in test set. Fig. S2. ROC (A) and DCA (B) curves of training and test sets for mortality prediction. Fig. S3. The SHAP summary plot of LightGBM model using “AKD grade” as a proxy for “renal function trajectories” in predicting mortality risk. Fig. S4. The SHAP interaction plot depicting the interactions among the top fifteen features of the lightGBM model for mortality prediction. Fig. S5. The SHAP dependence plots illustrate the correlations between key features in the prediction of mortality. A depicts the correlation between renal function trajectories and ACEI/ARB in predicting mortality. The x-axis represents the actual values of renal function trajectories, whereas the y-axis shows the SHAP values for trajectories, with values above zero suggesting an increased risk of mortality. Each dot represents an individual case, with the color transitioning from blue to red to indicate whether ACEI/ARB were taken or not. Specifically, the impact of ACEI/ARB on the mortality probability varies across different renal function trajectories. Among patients with normal kidney function, the use of ACEI/ARB is associated with a decrease in the risk of mortality. Conversely, for patients with AKD accompanied by AKI, the use of ACEI/ARB significantly increases the risk of mortality. B Illustrates the correlation between baseline eGFR and ACEI/ARB in predicting mortality. Among patients with lower baseline eGFR levels, the use of ACEI/ARB is associated with a slight increase in the risk of mortality. C Depicts the correlation between neutrophil count and renal function trajectories in predicting mortality. D Depicts the correlation between neutrophil count and antibiotics in predicting mortality. Fig. S6. The SHAP decision plots provided a detailed view of the inner workings of the lightGBM model. A, B provides personalized explanations for two cases with mortality probabilities below 10% and actual outcomes of survival. The direction of the line visualizes the decision process of the LightGBM model from the base value to the predicted value. The values adjacent to the line denote the measured values of the features. C, D provides personalized explanations for two cases with mortality probabilities exceeding 90% and actual outcomes of death. Table S1. Characteristics of hospital encounters in the study sample, overall and according to mortality [mean ± SD; n (%)]. Table S2. Performance of eight ML models for predicting mortality. Table S3. Performance of the LightGBM model for predicting mortality in the test set without data balancing.