Abstract

Advances in sensing technology have generated multi-modality datasets with complementary information in various domains. In health care, it is common to acquire images of different types/modalities for the same patient to facilitate clinical decision making. We propose a clustering method called hierarchically-structured Factor Mixture Model (hierFMM) that enables cluster discovery from multi-modality datasets to exploit their joint strength. HierFMM employs a novel double-L₂₁-penalized likelihood formulation to achieve hierarchical selection of modalities and features that are nested within the modalities. This formulation is proven to satisfy a Quadratic Majorization condition that allows for an efficient Group-wise Majorization Descent algorithm to be developed for model estimation. Simulation studies show significantly better performance of hierFMM than competing methods. HierFMM is applied to an application of identifying clusters/subgroups of migraine patients based on brain cortical area, thickness, and volume datasets extracted from Magnetic Resonance Imaging. Two subgroups are found, whose patients significantly differ in clinical characteristics. This finding shows the promise of using multi-modality imaging data to help patient stratification and develop optimal treatment for different subgroups with migraine.

摘要

传感技术的进步已经产生了具有多种领域补充信息的多模态数据集。在医疗保健中，通常为同一名患者获取不同类型/方式的图像以促进临床决策。我们提出了一种称为分层结构的因子混合模型（hierFMM）的聚类方法，该方法可从多模态数据集中发现聚类，以利用它们的联合强度。HierFMM采用了新颖的double-L ₂₁-惩罚似然公式，以实现模态和嵌套在模态内的特征的分层选择。事实证明，该公式满足二次主化条件，该条件允许开发有效的逐组主化下降算法以进行模型估计。仿真研究表明，hierFMM的性能明显优于竞争方法。HierFMM用于基于从磁共振成像中提取的大脑皮层面积，厚度和体积数据集来识别偏头痛患者的集群/亚组的应用。发现了两个亚组，其患者的临床特征明显不同。