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Patient outcome prediction is critical in management of ischemic stroke. Inthis paper, a novel machine learning model is proposed for stroke outcomeprediction using multimodal Magnetic Resonance Imaging (MRI). The proposedmodel consists of two serial levels of Autoencoders (AEs), where different AEsat level 1 are used for learning unimodal features from different MRImodalities and a AE at level 2 is used to combine the unimodal features intocompressed multimodal features. The sequences of multimodal features of a givenpatient are then used by an LSTM network for predicting outcome score. Theproposed AE2-LSTM model is proved to be an effective approach for betteraddressing the multimodality and volumetric nature of MRI data. Experimentalresults show that the proposed AE2-LSTM outperforms the existing state-of-theart models by achieving highest AUC=0.71 and lowest MAE=0.34.

A Novel Autoencoders-LSTM Model for Stroke Outcome Prediction using Multimodal MRI Data