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The claims data, containing medical codes, services information, and incurredexpenditure, can be a good resource for estimating an individual's healthcondition and medical risk level. In this study, we developed Transformer-basedMultimodal AutoEncoder (TMAE), an unsupervised learning framework that canlearn efficient patient representation by encoding meaningful information fromthe claims data. TMAE is motivated by the practical needs in healthcare tostratify patients into different risk levels for improving care delivery andmanagement. Compared to previous approaches, TMAE is able to 1) modelinpatient, outpatient, and medication claims collectively, 2) handle irregulartime intervals between medical events, 3) alleviate the sparsity issue of therare medical codes, and 4) incorporate medical expenditure information. Wetrained TMAE using a real-world pediatric claims dataset containing more than600,000 patients and compared its performance with various approaches in twoclustering tasks. Experimental results demonstrate that TMAE has superiorperformance compared to all baselines. Multiple downstream applications arealso conducted to illustrate the effectiveness of our framework. The promisingresults confirm that the TMAE framework is scalable to large claims data and isable to generate efficient patient embeddings for risk stratification andanalysis.

Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis