Kawasaki disease (KD) is the leading cause of acquired heart disease in children. Its prompt treatment can effectively lower the risk of severe complications, such as coronary aneurysms. However, accurately diagnosing KD at its early stage is impracticable given its unknown pathogenesis and lack of pathognomonic features. In this study, we investigated data-driven approaches by using a cohort of 10,367 patients extracted from electronic health records for early KD assessment. The incompleteness of clinical data presents group-based missing patterns associated with different clinical assessment measures. To address this problem, we developed a method integrating feature clustering to enable matrix-based representation and convolutional neural networks (CNN) for feature extraction and fusion to explicitly exploit the multi-source data structure. Integrating missing data imputation methods with the proposed method demonstrated superior accuracy (an AUC of 0.97) compared with a number of benchmark methods. The present method shows potential to improve clinical data mining. Our study highlighted the feasible utilization of matrix-based feature representation and CNN-based feature extraction for incomplete clinical data mining to support medical decision-making.

川崎病 (KD) 是儿童获得性心脏病的主要原因。其及时治疗可有效降低冠状动脉瘤等严重并发症的风险。然而，鉴于其发病机制未知且缺乏病理特征，在早期准确诊断 KD 是不切实际的。在这项研究中，我们使用从电子健康记录中提取的 10,367 名患者队列来研究数据驱动的方法，以进行早期 KD 评估。临床数据的不完整性呈现出与不同临床评估措施相关的基于组的缺失模式。为了解决这个问题，我们开发了一种集成特征聚类的方法，以启用基于矩阵的表示和卷积神经网络 (CNN) 进行特征提取和融合，以明确利用多源数据结构。与许多基准方法相比，将缺失数据插补方法与所提出的方法相结合显示出更高的准确性（AUC 为 0.97）。本方法显示出改进临床数据挖掘的潜力。我们的研究强调了将基于矩阵的特征表示和基于 CNN 的特征提取用于不完整临床数据挖掘以支持医疗决策的可行性。