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Clinical outcome prediction plays an important role in stroke patientmanagement. From a machine learning point-of-view, one of the main challengesis dealing with heterogeneous data at patient admission, i.e. the image datawhich are multidimensional and the clinical data which are scalars. In thispaper, a multimodal convolutional neural network - long short-term memory(CNN-LSTM) based ensemble model is proposed. For each MR image module, adedicated network provides preliminary prediction of the clinical outcome usingthe modified Rankin scale (mRS). The final mRS score is obtained by merging thepreliminary probabilities of each module dedicated to a specific type of MRimage weighted by the clinical metadata, here age or the National Institutes ofHealth Stroke Scale (NIHSS). The experimental results demonstrate that theproposed model surpasses the baselines and offers an original way toautomatically encode the spatio-temporal context of MR images in a deeplearning architecture. The highest AUC (0.77) was achieved for the proposedmodel with NIHSS.

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients