  Modern medicine requires generalised approaches to the synthesis and
integration of multimodal data, often at different biological scales, that can
be applied to a variety of evidence structures, such as complex disease
analyses and epidemiological models. However, current methods are either slow
and expensive, or ineffective due to the inability to model the complex
relationships between data modes which differ in scale and format. We address
these issues by proposing a cross-modal deep learning architecture and
co-attention mechanism to accurately model the relationships between the
different data modes, while further reducing patient diagnosis time.
Differentiating Parkinson's Disease (PD) patients from healthy patients forms
the basis of the evaluation. The model outperforms the previous
state-of-the-art unimodal analysis by 2.35%, while also being 53% more
parameter efficient than the industry standard cross-modal model. Furthermore,
the evaluation of the attention coefficients allows for qualitative insights to
be obtained. Through the coupling with bioinformatics, a novel link between the
interferon-gamma-mediated pathway, DNA methylation and PD was identified. We
believe that our approach is general and could optimise the process of medical
evidence synthesis and decision making in an actionable way.
