Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decision support systems, in particular computer-aided diagnosis (CADx) using machine learning (ML). Numerous ML approaches for CADx have been proposed in literature. However, these approaches assume feature-complete data, which is often not the case in clinical data. To account for missing data, incomplete data samples are either removed or imputed, which could lead to data bias and may negatively affect classification performance. As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multi-graph Geometric Matrix Completion (MGMC). MGMC uses multiple recurrent graph convolutional networks, where each graph represents an independent population model based on a key clinical meta-feature like age, sex, or cognitive function. Graph signal aggregation from local patient neighborhoods, combined with multi-graph signal fusion via self-attention, has a regularizing effect on both matrix reconstruction and classification performance. Our proposed approach is able to impute class relevant features as well as perform accurate and robust classification on two publicly available medical datasets. We empirically show the superiority of our proposed approach in terms of classification and imputation performance when compared with state-of-the-art approaches. MGMC enables disease prediction in multimodal and incomplete medical datasets. These findings could serve as baseline for future CADx approaches which utilize incomplete datasets.

大规模的基于人群的医学研究是更好地诊断、监测和治疗疾病的关键资源。它们还充当临床决策支持系统的推动者，特别是使用机器学习 (ML) 的计算机辅助诊断 (CADx)。文献中已经提出了许多用于 CADx 的 ML 方法。然而，这些方法假设特征完整的数据，这在临床数据中通常不是这种情况。为了解决缺失数据，不完整的数据样本被删除或估算，这可能导致数据偏差并可能对分类性能产生负面影响。作为一种解决方案，我们提出了一种通过多图几何矩阵完成（MGMC）对不完整医学数据集进行插补和疾病预测的端到端学习。MGMC 使用多个循环图卷积网络，其中每个图代表一个基于关键临床元特征（如年龄、性别或认知功能）的独立人口模型。来自局部患者邻域的图信号聚合，结合通过自我注意的多图信号融合，对矩阵重建和分类性能都有正则化作用。我们提出的方法能够估算与类别相关的特征，并对两个公开可用的医学数据集进行准确而稳健的分类。与最先进的方法相比，我们凭经验证明了我们提出的方法在分类和插补性能方面的优越性。MGMC 可以在多模式和不完整的医学数据集中进行疾病预测。这些发现可以作为未来使用不完整数据集的 CADx 方法的基线。