Fig. 4
From: Interpretable machine learning models for prolonged Emergency Department wait time prediction
Partial dependency plots of leading predictors from XGBoost. Figure 4 depicts Partial Dependency Plots (PDP) generated using the XGBoost algorithmic model to predict patient wait time. The categorical features include mode of arrival (moa) by ambulance (Panel A) and ED crowding status (overly crowded, Panel B), while age is presented as a continuous feature (Panel C). The categorical features demonstrate bidirectional effects on patient wait time prediction. Generally, patients who arrived at the ED under not overly crowded conditions, as well as those who arrived by ambulance, experienced shorter wait times, whereas patients arriving at an overly crowded ED, or arrived not by ambulance, experienced longer wait times. The PDP for age reveals a complex pattern; patients at extreme ages (i.e., very young or very old) tended to experience shorter wait times compared to others