Nerrise Favour, Heiman Alice Louise, Adeli Ehsan
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Department of Computer Science, Stanford University, Stanford, CA, USA.
Graphs Biomed Image Anal (2024). 2025;15182:57-68. doi: 10.1007/978-3-031-83243-7_6. Epub 2025 Mar 1.
The rapid advancement of medical technology has led to an exponential increase in multi-modal medical data, including imaging, genomics, and electronic health records (EHRs). Graph neural networks (GNNs) have been widely used to represent this data due to their prominent performance in capturing pairwise relationships. However, the heterogeneity and complexity of multi-modal medical data still pose significant challenges for standard GNNs, which struggle with learning higher-order, non-pairwise relationships. This paper proposes (raph-based nalysis of ulti-modal otor Impairment ssessments in arkinson's isease), a novel heterogeneous hypergraph fusion framework for multi-modal clinical data analysis. integrates imaging and non-imaging data into a "hypernetwork" (patient population graph) by preserving higher-order information and similarity between patient profiles and symptom subtypes. We also design a feature-based attention-weighted mechanism to interpret feature-level contributions towards downstream decision tasks. We evaluate our approach with clinical data from the Parkinson's Progression Markers Initiative (PPMI) and a private dataset. We demonstrate gains in predicting motor impairment symptoms in Parkinson's disease. Our end-to-end framework also learns associations between subsets of patient characteristics to generate clinically relevant explanations for disease and symptom profiles. The source code is available at https://github.com/favour-nerrise/GAMMA-PD.
医学技术的快速发展导致多模态医学数据呈指数级增长，包括成像、基因组学和电子健康记录（EHRs）。图神经网络（GNNs）因其在捕捉成对关系方面的卓越性能而被广泛用于表示此类数据。然而，多模态医学数据的异质性和复杂性仍然给标准GNNs带来了重大挑战，标准GNNs在学习高阶、非成对关系方面存在困难。本文提出了（帕金森病多模态运动障碍评估的基于图的分析），一种用于多模态临床数据分析的新型异构超图融合框架。通过保留患者概况和症状亚型之间的高阶信息和相似性，将成像和非成像数据集成到一个“超网络”（患者群体图）中。我们还设计了一种基于特征的注意力加权机制，以解释对下游决策任务的特征级贡献。我们使用来自帕金森病进展标记倡议（PPMI）的临床数据和一个私有数据集对我们的方法进行评估。我们展示了在预测帕金森病运动障碍症状方面的进展。我们的端到端框架还学习患者特征子集之间的关联，以生成与疾病和症状概况相关的临床解释。源代码可在https://github.com/favour-nerrise/GAMMA-PD获取。
