DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling

TL;DR

DeepJ is a novel graph convolutional transformer model with differentiable pooling that captures complex, cross-encounter interactions in EHR data, improving patient outcome prediction and interpretability.

Contribution

It introduces DeepJ, a new model combining graph convolutional transformers with differentiable pooling to model longitudinal medical event interactions.

Findings

DeepJ outperforms five baseline models in predictive accuracy.

DeepJ enhances interpretability of patient data.

It effectively identifies key event clusters across encounters.

Abstract

In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, existing graph-based approaches often work in a static manner, either restricting interactions within individual encounters or collapsing all historical encounters into a single snapshot. As a result, when it is necessary to identify meaningful groups of medical events spanning longitudinal encounters, existing methods are inadequate in modeling interactions cross encounters while accounting for temporal dependencies. To address this limitation, we introduce Deep Patient Journey (DeepJ), a novel graph convolutional transformer model with differentiable graph pooling to effectively capture intra-encounter and inter-encounter medical event interactions. DeepJ can identify groups of temporally and functionally related medical events, offering valuable insights into key event clusters pertinent to patient outcome prediction. DeepJ significantly outperformed five state-of-the-art baseline models while enhancing interpretability, demonstrating its potential for improved patient risk stratification.