Explainable ML model in clinical practice
Explainable ML model can make clinical practice more accurate. When the characteristics of each patient are entered into the model, the model first predicts whether he or she is at high risk. After that, we can understand the decision-making process of the model through SHAP analysis. According to the weight of different features and thresholds of features, physicians are also able to make rational clinical decisions. If there is any error in decision-making process, physicians can clearly see the unreasonable judgements in model prediction, to combine model outputs and clinical experience to further confirm whether patient is at high risk. Besides, with the increase of sample size in the training cohort of model, the accuracy of the model will also be improved. Perhaps human–computer interaction will become a trend in clinical practice in the future.