标题： 多模态神经退行性疾病亚型解释由ChatGPT 显示英文标题

标题： Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT

摘要： 阿尔茨海默病（AD）是最常见的神经退行性疾病；然而，目前可用的治疗方法仅限于阻止疾病进展。此外，由于疾病的异质性，这些治疗的效果并不能保证。因此，能够在非常早期阶段识别疾病亚型至关重要。当前的数据驱动方法能够在AD或相关疾病的后期阶段对亚型进行分类，但在无症状或前驱阶段预测时却存在困难。此外，大多数现有模型要么缺乏分类背后的可解释性，要么仅使用单一模态进行评估，限制了分析范围。因此，我们提出了一种多模态框架，利用影像学、遗传学和临床评估等早期阶段指标，在早期阶段将AD患者分类为亚型。同样，我们构建提示并使用大型语言模型，如ChatGPT，来解释我们模型的发现。在我们的框架中，我们提出了一种三模态协同注意力机制（Tri-COAT），以显式学习跨模态特征关联。我们提出的模型优于基线模型，并提供了由已知生物机制支持的关键跨模态特征关联的见解。 显示英文摘要

摘要： Alzheimer's disease (AD) is the most prevalent neurodegenerative disease; yet its currently available treatments are limited to stopping disease progression. Moreover, effectiveness of these treatments is not guaranteed due to the heterogenetiy of the disease. Therefore, it is essential to be able to identify the disease subtypes at a very early stage. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. Moreover, most existing models either lack explainability behind the classification or only use a single modality for the assessment, limiting scope of its analysis. Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages. Similarly, we build prompts and use large language models, such as ChatGPT, to interpret the findings of our model. In our framework, we propose a tri-modal co-attention mechanism (Tri-COAT) to explicitly learn the cross-modal feature associations. Our proposed model outperforms baseline models and provides insight into key cross-modal feature associations supported by known biological mechanisms.