Transformer is the latest deep neural network (DNN) architecture for sequence data learning that has revolutionized the field of natural language processing. This success has motivated researchers to explore its application in the healthcare domain. Despite the similarities between longitudinal clinical data and natural language data, clinical data presents unique complexities that make adapting Transformer to this domain challenging. To address this issue, we have designed a new Transformer-based DNN architecture, referred to as Hybrid Value-Aware Transformer (HVAT), which can jointly learn from longitudinal and non-longitudinal clinical data. HVAT is unique in the ability to learn from the numerical values associated with clinical codes/concepts such as labs, and also the use of a flexible longitudinal data representation called clinical tokens. We trained a prototype HVAT model on a case-control dataset, achieving high performance in predicting Alzheimer's disease and related dementias as the patient outcome. The result demonstrates the potential of HVAT for broader clinical data learning tasks.

The authors have declared no competing interest.

This study was funded by Grant NIH/NIA RF1AG069121 and Grant NIH/NIA 1R01AG073474-01A1.

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Ethics committee/IRB of Washington DC Veterans Affairs Medial Center gave ethical approval for this work

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