PCPs: Patient Cardiac Prototypes
This work addresses the problem of limited clinical utility of population-based, opaque deep learning models for physicians by providing patient-specific, interpretable representations.
This paper proposes Patient Cardiac Prototypes (PCPs), patient-specific embeddings that summarize cardiac state. They enable discovery of similar patients, generation of patient-specific diagnoses via a hypernetwork, and act as a compact substitute for dataset distillation.
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.