Fig 1.
Cohort attrition diagram.
Table 1.
Cohort demographic and clinical characteristics.
Fig 2.
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves describing model performance in identifying PASC with (MIS-C or non-MIS-C variants).
For each of the three outcomes (PASC (any), non MIS-C PASC, and MIS-C) the Receiver Operating Characteristic (ROC) curves and Precision-Recall (PR) curves are estimated and plotted 5 times, once for each cross-validation fold.
Table 2.
Machine learning model performance in identifying patients with PASC (MIS-C or non-MIS-C variants).
The table displays several performance metrics for each of the three outcomes (PASC (any), non MIS-C PASC, and MIS-C). Accuracy, F1 score, precision, and recall were all computed at the p = 0.5 threshold. In addition to composite performance statistics, we report the area under the precision-recall curve (AUPR) and area under the receiver operating characteristic curve (AUROC). The curves themselves are shown and described in Fig 2.
Fig 3.
SHapley Additive exPlanation (SHAP) values for top administrative and clinical model features by class in predicting non-MIS-C PASC.
The plots show the most significant features as determined by the sum of SHAP value magnitudes over all samples. For each feature, SHAP values for each patient are plotted, with color representing the feature value (e.g. red if feature was present and blue if absent in case of a binary variable). For the SHAP values pictured, the x axis is interpreted as change in log odds (in particular, SHAP values are not confined to be between –1 and 1).