Figure 1.

Data and modeling schemas

(A) Architecture with CL. EHR data are modeled to create patient and event embedding representations, which are fed into our CL equation.

(B) Representation space. CL simultaneously pushes positive patients and event embeddings (i.e., concordant with respect to the outcome of the patient of interest, respectively) away from negative ones.

(C) Time binning. Schematic to visualize how we model time sequence. We have two outcome windows (i.e., 24and 48 h prior to event) and bin data by 6-h chunks.

(D) Selection of event timing for null outcomes. For patients that do not experience the outcome of interest, we generate a data-driven event time to align against as in (C). We compute the mean and standard deviation for the length of time that elapsed from admission for all patients with the affiliated outcomes independently. For patients without an event, we randomly pick a time to use as a reference end point using a Gaussian distribution with the mean and standard deviation obtained from the positive training data.