Figure 3:

Contribution of parameters to our model’s performance. Shown is an evaluation of the contribution of the CBC parameters to the performance of our model. (Specifically, to the AUC measure at the 0–30 day and 90–180 day time windows.) When evaluating the importance of a parameter, we address both its direct contribution to the performance measure as well as its redundancy with the other model parameters (i.e., the degree to which its contribution can be replaced by other parameters). Thus, each parameter is assigned a point in a 2-dimensional space of redundancy (horizontal axis) and direct contribution (vertical axis). To this end, we remove other parameters from the model one by one, ordered by their correlation to the parameter in question. At each step we determine the performance of the submodel with and without the parameter in question and calculate the difference. The redundancy of the parameter is the minimal number of such steps required before the difference is significant (defined by 2 standard deviations as estimated by the bootstrapping process), and the direct contribution is defined by the maximal difference. We show only parameters that achieve significant contribution at some point. We found that the red blood cell line parameters are the main contributors, that platelet-related parameters contribute less and are more redundant with other parameters, and that the white blood cell line parameters contribute mainly at the 0–30 day time window.