Predicting Physiological Response in Heart Failure Management: A Graph Representation Learning Approach using Electronic Health Records

Abstract

Heart failure management is challenging due to the complex and heterogenous nature of its pathophysiology which makes the conventional treatments based on the "one size fits all" ideology not suitable. Coupling the longitudinal medical data with novel deep learning and network-based analytics will enable identifying the distinct patient phenotypic characteristics to help individualize the treatment regimen through the accurate prediction of the physiological response. In this study, we develop a graph representation learning framework that integrates the heterogeneous clinical events in the electronic health records (EHR) as graph format data, in which the patient-specific patterns and features are naturally infused for personalized predictions of lab test response. The framework includes a novel Graph Transformer Network that is equipped with a self-attention mechanism to model the underlying spatial interdependencies among the clinical events characterizing the cardiac physiological interactions in the heart failure treatment and a graph neural network (GNN) layer to incorporate the explicit temporality of each clinical event, that would help summarize the therapeutic effects induced on the physiological variables, and subsequently on the patient's health status as the heart failure condition progresses over time. We introduce a global attention mask that is computed based on event co-occurrences and is aggregated across all patient records to enhance the guidance of neighbor selection in graph representation learning. We test the feasibility of our model through detailed quantitative and qualitative evaluations on observational EHR data.

AI and the next medical revolution: deep learning’s uncharted healthcare promise

“…Raghu et al [72] employed policy gradient reinforcement learning on medical ontologies for recommending optimal treatments for sepsis patients. Chowdhury et al [73] developed a deep reinforcement recommendation (DRR) model that leverages both static and sequential patient information.…”

“…ReferenceMethod Outcome[72] Reinforcement Learning for Treatment Policies Optimizes treatment policies; used for sepsis treatment and general treatment guidance [73]. …”