Machine learning (ML) has the potential to become an essential tool in supporting clinical decision-making processes, offering enhanced diagnostic capabilities and personalized treatment plans. However, outsourcing medical records to train ML models using patient data raises legal, privacy, and security concerns. Federated learning has emerged as a promising paradigm for collaborative ML, meeting healthcare institutions' requirements for robust models without sharing sensitive data and compromising patient privacy. This study proposes a novel method that combines federated learning (FL) and Graph Neural Networks (GNNs) to predict stroke severity using electroencephalography (EEG) signals across multiple medical institutions. Our approach enables multiple hospitals to jointly train a shared GNN model on their local EEG data without exchanging patient information. Specifically, we address a regression problem by predicting the National Institutes of Health Stroke Scale (NIHSS), a key indicator of stroke severity. The proposed model leverages a masked self-attention mechanism to capture salient brain connectivity patterns and employs EdgeSHAP to provide post-hoc explanations of the neurological states after a stroke. We evaluated our method on EEG recordings from four institutions, achieving a mean absolute error (MAE) of 3.23 in predicting NIHSS, close to the average error made by human experts (MAE $\approx$ 3.0). This demonstrates the method's effectiveness in providing accurate and explainable predictions while maintaining data privacy.

翻译：机器学习（ML）有潜力成为支持临床决策过程的重要工具，提供增强的诊断能力和个性化治疗方案。然而，将医疗记录外包以使用患者数据训练ML模型会引发法律、隐私和安全方面的担忧。联邦学习已成为一种有前景的协同机器学习范式，能够在无需共享敏感数据且不损害患者隐私的前提下，满足医疗机构对鲁棒模型的需求。本研究提出了一种新颖方法，结合联邦学习（FL）和图神经网络（GNNs），利用跨多个医疗机构的脑电图（EEG）信号预测卒中严重程度。我们的方法使多家医院能够在本地EEG数据上联合训练共享的GNN模型，而无需交换患者信息。具体而言，我们通过预测美国国立卫生研究院卒中量表（NIHSS）——卒中严重程度的关键指标——来解决回归问题。所提出的模型利用掩码自注意力机制捕捉显著的大脑连接模式，并采用EdgeSHAP提供卒中后神经状态的事后解释。我们在来自四个机构的EEG记录上评估了该方法，在预测NIHSS时取得了3.23的平均绝对误差（MAE），接近人类专家的平均误差（MAE $\approx$ 3.0）。这证明了该方法在保持数据隐私的同时，能够提供准确且可解释的预测的有效性。