  Many clinical deep learning algorithms are population-based and difficult to
interpret. Such properties limit their clinical utility as population-based
findings may not generalize to individual patients and physicians are reluctant
to incorporate opaque models into their clinical workflow. To overcome these
obstacles, we propose to learn patient-specific embeddings, entitled patient
cardiac prototypes (PCPs), that efficiently summarize the cardiac state of the
patient. To do so, we attract representations of multiple cardiac signals from
the same patient to the corresponding PCP via supervised contrastive learning.
We show that the utility of PCPs is multifold. First, they allow for the
discovery of similar patients both within and across datasets. Second, such
similarity can be leveraged in conjunction with a hypernetwork to generate
patient-specific parameters, and in turn, patient-specific diagnoses. Third, we
find that PCPs act as a compact substitute for the original dataset, allowing
for dataset distillation.
