Transformers provide a novel approach for unifying large-scale biobank data spread
across different modalities and omic domains. We introduce Modular Quantitative Temporal
Transformer (MQTT), a modular architecture for multimodal data that offers a robust
fusion mechanism for biobank data, which can integrate (1) systematically missing
modality data, (2) quantitative data, and (3) longitudinal data. We apply the model
for the fusion of personal, laboratory, diagnostic, clinical, and drug prescription
data from the UK Biobank. We investigate MQTT's modular representations, focusing
on multimorbidity and polypharmacy. We demonstrate its applicability by a novel stratification
of major depressive disorder resulting in subtypes related to treatment-resistant
depression with novel genes significant at the GWAS level.
