Predicting the physiological effects of multiple drugs using electronic health record

Supplementary Data 1. Optimized hyperparameters of XGBoost classifiers for the 20 selected measurement items. Supplementary Data 2. Information on 416 active pharmaceutical ingredients used to develop machine learning models for the 20 selected measurement items. Supplementary Data 3. List of top 20 features with the highest feature importance for each type of machine learning models according to the SHAP analysis (Fig. 4). Numbers such as “1801186” and “1362055” are APIs presented using the old version of RxCUI.补充数据1。针对20项选定测量指标的XGBoost分类器优化超参数。 补充数据2。用于为20项选定测量指标构建机器学习模型的416种原料药（Active Pharmaceutical Ingredient，API）相关信息。 补充数据3。基于SHAP分析（图4）得到的各类型机器学习模型的前20个最高特征重要性特征列表。诸如"1801186"与"1362055"的编号为采用旧版RxNorm概念唯一标识符（RxCUI）表示的原料药。