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Recent deep learning research based on Transformer model architectures hasdemonstrated state-of-the-art performance across a variety of domains andtasks, mostly within the computer vision and natural language processingdomains. While some recent studies have implemented Transformers for clinicaltasks using electronic health records data, they are limited in scope,flexibility, and comprehensiveness. In this study, we propose a flexibleTransformer-based EHR embedding pipeline and predictive model framework thatintroduces several novel modifications of existing workflows that capitalize ondata attributes unique to the healthcare domain. We showcase the feasibility ofour flexible design in a case study in the intensive care unit, where ourmodels accurately predict seven clinical outcomes pertaining to readmission andpatient mortality over multiple future time horizons.

Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers