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arXiv · 2404.12634

Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

Danqing Ma·Meng Wang·Ao Xiang·Zongqing Qi·Qin Yang
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Abstract

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects..

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Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi, Qin Yang. 2024-11-16. Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment. https://arxiv.org/abs/2404.12634

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