用超图预训练提升心房颤动预测准确率
Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training
- 构建超图模型捕捉患者间高阶关联特征
- 在7780例中预训练，迁移至510例ESUS患者
- 显著提升小样本下心房颤动预测效果
心房颤动（AF）是不明原因栓塞性卒中（ESUS）后的重要并发症，增加复发卒中和死亡风险。早期识别至关重要，但现有工具在准确性、可扩展性和成本方面存在局限。机器学习虽具潜力，却受限于小规模ESUS队列和高维医疗特征。为此，我们提出监督与非监督的超图预训练策略，以改进ESUS患者的AF预测。首先，在大规模卒中队列（7,780例）上预训练超图患者嵌入模型，捕捉关键特征与高阶交互关系；所得嵌入被迁移至小规模ESUS队列（510例），降低特征维度的同时保留临床意义信息，使轻量级模型实现高效预测。实验表明，两种预训练方法均优于基于原始数据的传统模型，提升了准确率与鲁棒性。该框架为卒中后AF风险预测提供了可扩展、高效的解决方案。
原文摘要 · Abstract (English)
Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification is clinically important, yet existing tools face limitations in accuracy, scalability, and cost. Machine learning (ML) offers promise but is hindered by small ESUS cohorts and high-dimensional medical features. To address these challenges, we introduce supervised and unsupervised hypergraph-based pre-training strategies to improve AF prediction in ESUS patients. We first pre-train hypergraph-based patient embedding models on a large stroke cohort (7,780 patients) to capture salient features and higher-order interactions. The resulting embeddings are transferred to a smaller ESUS cohort (510 patients), reducing feature dimensionality while preserving clinically meaningful information, enabling effective prediction with lightweight models. Experiments show that both pre-training approaches outperform traditional models trained on raw data, improving accuracy and robustness. This framework offers a scalable and efficient solution for AF risk prediction after stroke.
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