Abstract
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.
Abstract (translated)
机器学习（ML）有可能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模型引发了法律、隐私和安全方面的担忧。联邦学习作为一种有前景的合作式机器学习范例应运而生，它能够在不分享敏感数据且不影响患者隐私的情况下满足医疗机构对稳健模型的需求。本研究提出了一种结合联邦学习（FL）和图神经网络（GNNs）的新方法，用于跨多个医疗结构使用脑电图（EEG）信号预测中风的严重程度。我们的方法使多家医院能够在不交换患者信息的情况下共同训练一个共享的GNN模型。具体而言，我们通过预测美国国立卫生研究院中风量表（NIHSS），这是一个衡量中风严重程度的关键指标，解决了一个回归问题。所提出的模型利用了掩码自注意力机制来捕捉显著的大脑连接模式，并采用EdgeSHAP提供中风后神经状态的解释。我们在四个机构的EEG记录上评估了我们的方法，在预测NIHSS时达到了3.23的平均绝对误差（MAE），接近人类专家的平均误差水平（MAE ≈ 3.0）。这证明了该方法在保持数据隐私的同时，提供了准确且可解释的预测。
URL
https://arxiv.org/abs/2411.02286
