Figure 3 –

Relative variable importance for model predictions

Beeswarm plots demonstrating the relative importance of the top 20 variables to all model predictions for the internal validation cohort. Each value for each variable observed (i.e., patient) in the cohort is shown as a single dot, colored by value and with position on the x-axis indicating the impact that that value of the variable had on the model’s prediction for that patient in logit space (i.e. Shapley value). Variables with wide spread have large effect on model predictions. Color indicates whether low or high values of each variable impact risk and in which direction. For example, pink colors to the right of midline (i.e., impact on model output > 0) suggest that high values of the variable increase the model’s predicted risk of transfusion. For categorical variables such as patient comorbidities, pink indicates the presence of that variable and blue is the absence. Variables with blue or pink colors on both sides of midline indicate that that variable can either increase or decrease transfusion risk, depending on interactions with other variables. For example, patients with low platelet count are shown with blue dots that appear both to the left and right of midline, indicating that low platelet count can either increase or decrease predicted transfusion risk depending on each patient’s other characteristics. Average impact on model prediction is shown on the right and indicates overall variable importance. This is computed as the mean absolute value of all Shapley values observed for that variable in the cohort.