使用多重图几何矩阵完成（MGMC）在不完整医学数据集中进行同时插补和疾病分类
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
摘要：
大规模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。它们还可以作为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）。在文献中已经提出了许多用于CADx的ML方法。然而，这些方法假设完整的数据可用性，这在临床数据中并不总是可行的。为了解释缺失数据，不完整的数据样本被删除或估算，这可能导致数据偏倚，并可能对分类性能产生负面影响。作为解决方案，我们提出了一种通过多重图几何矩阵完成（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使用多个递归图卷积网络，其中每个图表示基于关键临床元特征（如年龄，性别或认知功能）的独立人群模型。图信号聚合从当地的病人邻居，结合多图信号融合通过自我关注，有一个正规化的影响矩阵重建和分类性能。我们所提出的方法能够估算类相关的功能，以及执行两个公开的医疗数据集上的准确分类。我们的经验表明，我们提出的方法在分类和插补性能方面的优越性相比，国家的最先进的方法。MGMC可以在多模态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利用不完整数据集的CADx方法的基线。
英文摘要：
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 full data availability, which is not always feasible 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 Multigraph 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 multigraph 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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
