GatorCLR: Personalized predictions of patient outcomes on electronic health records using self-supervised contrastive graph representation.

Abstract

Recently, there has been growing interest in analyzing large amounts of Electronic Health Record (EHR) data. Patient outcome prediction is a major area of interest in EHR analysis that focuses on predicting the future health status of patients using structured data types, such as diagnoses, medications, and procedures collected from longitudinal EHR data. We investigate and design self-supervised learning (SSL) paradigms to learn high-quality representations from longitudinal EHR data, aiming to effectively capture longitudinal relationships and patterns for improved patient outcome predictions. We propose an end-to-end, novel, and robust model called GatorCLR that aligns with the contrastive SSL paradigm. GatorCLR incorporates graph analysis-based patient modeling into longitudinal EHR data, generating graph representations of nodes and edges representing patients, their relationships, and similarities. A two-layer augmentation technique is further incorporated in our GatorCLR that generates consistent, identity-preserving augmentations from graph representations. We evaluate our approach using real-world EHR datasets. Experimental results indicate that our GatorCLR delivers meaningful and robust performance across multiple clinical tasks and datasets and provides transparency of the model decisions. The proposed approach presents a significant step toward developing a foundation model with longitudinal EHR data, capable of making informed predictions and adaptable to various downstream use cases and tasks. This study should, therefore, be of value to practitioners wishing to leverage longitudinal EHR data for predictive analytics.