# Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

Source: [Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment](<https://www.sota2.com/research/paper/68fa75c11e209131cea69bd5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:40:43.406Z

Authors: Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi, Qin Yang

Published: 2024-04-19T05:31:37.000Z

## 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..

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.12634>)

[PDF](<https://arxiv.org/pdf/2404.12634.pdf>)