  Recent randomised clinical trials have shown that patients with ischaemic
stroke {due to occlusion of a large intracranial blood vessel} benefit from
endovascular thrombectomy. However, predicting outcome of treatment in an
individual patient remains a challenge. We propose a novel deep learning
approach to directly exploit multimodal data (clinical metadata information,
imaging data, and imaging biomarkers extracted from images) to estimate the
success of endovascular treatment. We incorporate an attention mechanism in our
architecture to model global feature inter-dependencies, both channel-wise and
spatially. We perform comparative experiments using unimodal and multimodal
data, to predict functional outcome (modified Rankin Scale score, mRS) and
achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy
for individual mRS scores.
