Parkinson’s disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity. This study introduces an graph-based interpretable personalized progression method, utilizing data from the Parkinson’s Progression Markers Initiative (PPMI) and Stroke Parkinson’s Disease Biomarker Program (PDBP). Our approach integrates multimodal inter-individual and intra-individual data, including clinical assessments, MRI, and genetic information to make multi-dimension predictions. Validated using the PDBP dataset from 12 to 36 months, our AdaMedGraph method demonstrated strong performance, achieving AUC values of 0.748 and 0.714 for the 12-month Hoehn and Yahr Scale and Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III on the PPMI test set. Ablation analysis reveals the importance of baseline clinical assessment predictors. This novel framework improves personalized care and offers insights into unique disease trajectories in PD patients.

帕金森病 （PD） 是一种复杂的神经系统疾病，其特征是多巴胺能神经元变性，导致不同的运动和非运动障碍。这种可变性使准确的进展建模和早期预测变得复杂。基于临床症状的传统分类方法通常受到疾病异质性的限制。本研究介绍了一种基于图形的可解释个性化进展方法，利用来自帕金森病进展标志物计划 （PPMI） 和中风帕金森病生物标志物计划 （PDBP） 的数据。我们的方法整合了多模式的个身间和个体内数据，包括临床评估、MRI 和遗传信息，以做出多维预测。使用 12 至 36 个月的 PDBP 数据集进行验证，我们的 AdaMedGraph 方法表现出强大的性能，在 12 个月的 Hoehn 和 Yahr 量表和运动障碍协会赞助的统一帕金森病评定量表 （MDS-UPDRS） III 修订版中达到 0.748 和 0.714 的 AUC 值在 PPMI 测试集上。消融分析揭示了基线临床评估预测因子的重要性。这种新颖的框架改善了个性化护理，并提供了对 PD 患者独特疾病轨迹的见解。