PCPS: PATIENT CARDIAC PROTOTYPES

Abstract

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.

1. INTRODUCTION

Modern medical research is arguably anchored around the "gold standard" of evidence provided by randomized control trials (RCTs) (Cartwright, 2007) . However, RCT-derived conclusions are population-based and fail to capture nuances at the individual patient level (Akobeng, 2005) . This is primarily due to the complex mosaic that characterizes a patient from demographics, to physiological state, and treatment outcomes. Similarly, despite the success of deep learning algorithms in automating clinical diagnoses (Galloway et al., 2019; Attia et al., 2019a; b; Ko et al., 2020) , network-generated predictions remain population-based and difficult to interpret. Such properties are a consequence of a network's failure to incorporate patient-specific structure during training or inference. As a result, physicians are reluctant to integrate such systems into their clinical workflow. In contrast to such reluctance, personalized medicine, the ability to deliver the right treatment to the right patient at the right time, is increasingly viewed as a critical component of medical diagnosis (Hamburg & Collins, 2010) . The medical diagnosis of cardiac signals such as the electrocardiogram (ECG) is of utmost importance in a clinical setting (Strouse et al., 1939) . For example, such signals, which convey information about potential abnormalities in a patent's heart, also known as cardiac arrhythmias, are used to guide medical treatment both within and beyond the cardiovascular department (Carter, 1950) . In this paper, we conceptually borrow insight from the field of personalized medicine in order to learn patient representations which allow for a high level of network interpretability. Such representations have several potential clinical applications. First, they allow clinicians to quantify the similarity of patients. By doing so, network-generated predictions for a pair of patients can be traced back to this similarity, and in turn, their corresponding ECG recordings. Allowing for this inspection of ECG recordings aligns well with the existing clinical workflow. An additional application of patient similarity is the exploration of previously unidentified patient relationships, those which may lead to the discovery of novel patient sub-cohorts. Such discoveries can lend insight into particular diseases and appropriate medical treatments. In contrast to existing patient representation learning methods (Zhu et al., 2016; Suo et al., 2017) , we concurrently optimize for a predictive task (cardiac arrhythmia classification), leverage patient similarity, and design a system specifically for 12-lead ECG signals. Contributions. Our contributions are the following: 1. Patient cardiac prototypes (PCPs) -we learn representations that efficiently summarize the cardiac state of a patient in an end-to-end manner via contrastive learning.

