  The claims data, containing medical codes, services information, and incurred
expenditure, can be a good resource for estimating an individual's health
condition and medical risk level. In this study, we developed Transformer-based
Multimodal AutoEncoder (TMAE), an unsupervised learning framework that can
learn efficient patient representation by encoding meaningful information from
the claims data. TMAE is motivated by the practical needs in healthcare to
stratify patients into different risk levels for improving care delivery and
management. Compared to previous approaches, TMAE is able to 1) model
inpatient, outpatient, and medication claims collectively, 2) handle irregular
time intervals between medical events, 3) alleviate the sparsity issue of the
rare medical codes, and 4) incorporate medical expenditure information. We
trained TMAE using a real-world pediatric claims dataset containing more than
600,000 patients and compared its performance with various approaches in two
clustering tasks. Experimental results demonstrate that TMAE has superior
performance compared to all baselines. Multiple downstream applications are
also conducted to illustrate the effectiveness of our framework. The promising
results confirm that the TMAE framework is scalable to large claims data and is
able to generate efficient patient embeddings for risk stratification and
analysis.
