  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.
