Temporal Clustering with External Memory Network for Disease Progression Modeling

TL;DR

This paper introduces TC-EMNet, a novel model combining variational autoencoders and external memory to improve disease progression modeling by capturing long-term dependencies and relevant events in EHR data, enabling effective patient clustering.

Contribution

The paper presents a new temporal clustering model with external memory that enhances disease progression analysis by explicitly capturing relevant long-term patient information.

Findings

Demonstrates competitive clustering performance on real datasets.

Identifies clinically meaningful disease clusters.

Produces superior patient state representations compared to baselines.

Abstract

Disease progression modeling (DPM) involves using mathematical frameworks to quantitatively measure the severity of how certain disease progresses. DPM is useful in many ways such as predicting health state, categorizing disease stages, and assessing patients disease trajectory etc. Recently, with wider availability of electronic health records (EHR) and the broad application of data-driven machine learning method, DPM has attracted much attention yet remains two major challenges: (i) Due to the existence of irregularity, heterogeneity and long-term dependency in EHRs, most existing DPM methods might not be able to provide comprehensive patient representations. (ii) Lots of records in EHRs might be irrelevant to the target disease. Most existing models learn to automatically focus on the relevant information instead of explicitly capture the target-relevant events, which might make the learned model suboptimal. To address these two issues, we propose Temporal Clustering with External Memory Network (TC-EMNet) for DPM that groups patients with similar trajectories to form disease clusters/stages. TC-EMNet uses a variational autoencoder (VAE) to capture internal complexity from the input data and utilizes an external memory work to capture long term distance information, both of which are helpful for producing comprehensive patient states. Last but not least, k-means algorithm is adopted to cluster the extracted comprehensive patient states to capture disease progression. Experiments on two real-world datasets show that our model demonstrates competitive clustering performance against state-of-the-art methods and is able to identify clinically meaningful clusters. The visualization of the extracted patient states shows that the proposed model can generate better patient states than the baselines.