Statistics and reproducibility
We used multiple statistical indices and empirical distributions to assess the performance of the proposed frameworks. The equations of the statistical indices are shown in Supplementary Fig. 21 and all the abbreviations used in this study are defined in Supplementary Table 23. All the data used in this study followed the criteria: (1) sign informed consent prior to enrollment. (2) At least 18 years old. This study was conducted following the declaration of Helsinki and was approved by the Capital Medical University Ethics Committee. The following statistical analyses of the data were conducted for both evaluating the classification framework and the regression framework.Statistical indices to evaluate the classification framework. Multiple evaluation indicators (PRC, ROC, AUPRC, AUROC, sensitivity, specificity, precision, kappa index, and F1 with a fixed threshold) were computed for a comprehensive and accurate assessment of the classification framework. Multiple threshold values were in the range from 0 to 1 with a step value of 0.005 to obtain the ROC and PRC curves. The PRC showed the relationship between the precision and the sensitivity (or recall), and the ROC indicated the relationship between the sensitivity and specificity. The two curves reflected the comprehensive performance of the classification framework. The kappa index is a statistical method for assessing the degree of agreement between different methods. In our use case, the indicator was used to measure the stability of the method. The F1 score is a harmonic average of precision and sensitivity and considers the FP and FN. The bootstrapping method was used to calculate the empirical distribution of each indicator. The detailed calculation process was as follows: we conducted random sampling with replacement to generate 1000 new test data sets with the same number of samples as the original test data set. The evaluation indicators were calculated to determine the distributions. The results were displayed in boxplots (Fig. 5 and Supplementary Fig. 2).
Statistical indices to evaluate the regression framework. Multiple evaluation indicators (MSE, RMSE, MAE, R2, and PCC) were computed for a comprehensive and accurate assessment of the regression framework. The MSE was used to calculate the deviation between the predicted and true values. The RMSE was the square root of the MSE result. The two indicators show the accuracy of the model prediction. The R2 was used to assess the goodness-of-fit of the regression framework. The r was used to assess the correlation between two variables in the regression framework. The indicators were calculated using the open-source tools scikit-learn and the scipy library.