Precision medicine tailored to individual patients has gained significant attention in recent times. Machine learning techniques are now employed to process personalized data from various sources, including images, genetics, and assessments. These techniques have demonstrated good outcomes in many clinical prediction tasks. Notably, the approach of constructing graphs by linking similar patients and then applying graph neural networks (GNNs) stands out, because related information from analogous patients are aggregated and considered for prediction. However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources. Previous studies rely on human expertise to select the edge feature, which is neither scalable nor efficient in pinpointing crucial edge features for complex diseases. In this paper, we propose a novel algorithm named \ours, which can automatically select important features to construct multiple patient similarity graphs, and train GNNs based on these graphs as weak learners in adaptive boosting. \ours{} is evaluated on two real-world medical scenarios and shows superiors performance.

翻译：针对个体患者的精准医疗近年来受到广泛关注。机器学习技术现已被用于处理来自图像、遗传学和评估等多种来源的个性化数据。这些技术在诸多临床预测任务中展现出良好效果。值得注意的是，通过连接相似患者构建图、进而应用图神经网络（GNNs）的方法尤为突出，因为该方法可聚合并利用来自类似患者的相关信息进行预测。然而，鉴于每位患者由来自不同来源的高维特征描述，如何选择合适的边特征来定义患者相似性并构建图具有挑战性。以往研究依赖人工经验选择边特征，这既不可扩展也难以准确定位复杂疾病的关键边特征。本文提出一种名为\ours{}的新颖算法，该算法能自动选择重要特征构建多个患者相似图，并基于这些图训练GNNs作为自适应提升中的弱学习器。\ours{}在两项真实医疗场景评估中展现出优越性能。