发表机构
Harrison College of Pharmacy, Auburn University; Fisk University; Lewis Katz School of Medicine, Temple University; Emory University; Barnett College of Public Health, Temple University（奥本大学哈里森药学院; 菲斯克大学; 天普大学刘易斯·卡茨医学院; 埃默里大学; 天普大学巴尼特公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对ALS预测临床有意义事件的挑战，开发受数字孪生启发的事件发生时间框架，整合多源数据，经聚类、建模等确定功能域及预测因素，构建TTE模型，能生成个性化生存曲线，为ALS相关决策支持提供可行方法。
AI 中文摘要
肌萎缩侧索硬化症（ALS）是一种进行性且异质性的神经退行性疾病，预测临床有意义的里程碑事件（如辅助设备使用）具有挑战性。我们开发了一个受数字孪生启发的事件发生时间框架，将纵向ALS功能评分量表修订版（ALSFRS-R）轨迹与生存模型相结合，以支持功能衰退和辅助设备使用的个性化预测。通过整合诊断记录、ALSFRS-R评估、日常生活活动和人口统计信息构建了一个协调的纵向数据集，并进行预处理以确保数据质量、时间对齐和队列一致性。基于相关性的聚类确定了跨越延髓、上肢、轴向、下肢和呼吸系统的连贯功能域。广义相加混合模型表征了所有域的非线性、特定域功能衰退。此外，开发了一个时间序列机器学习模型来预测纵向功能衰退并捕捉阶段依赖性疾病进展。Cox比例风险模型进一步确定下肢功能，特别是行走和爬楼梯，是早期使用轮椅的最强预测因素。在此基础上，我们实施了一个受数字孪生启发的基于时间序列机器学习的事件发生时间（TTE）模型，该模型生成个性化生存曲线并动态预测无轮椅生存。该框架为将ALS进展与个性化决策支持联系起来提供了一种可扩展、可解释且临床可行的方法，可应用于主动护理计划、临床试验分层和精准医学。
英文摘要
Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains challenging. We developed a time-to-event, digital-twin-inspired framework that integrates longitudinal ALS Functional Rating Scale-Revised (ALSFRS-R) trajectories with survival modeling to support individualized prediction of functional decline and assistive device utilization. We constructed a harmonized longitudinal dataset by integrating diagnosis records, ALSFRS-R assessments, activities of daily living, and demographic information, followed by preprocessing to ensure data quality, temporal alignment, and cohort consistency. Correlation-based clustering identified coherent functional domains spanning bulbar, upper limb, axial, lower limb, and respiratory systems. Generalized additive mixed models characterized nonlinear, domain-specific functional decline across all domains. In addition, a temporal machine learning model was developed to predict longitudinal functional decline and capture stage-dependent disease progression. Cox proportional hazards modeling further identified lower limb function, particularly walking and stair climbing, as the strongest predictors of earlier wheelchair access. Building on these results, we implemented a digital twin-inspired temporal machine learning-based time-to-event (TTE) model that generates individualized survival curves and dynamically predicts wheelchair-free survival. This framework provides a scalable, interpretable, and clinically actionable approach for linking ALS progression with personalized decision support, with applications in proactive care planning, clinical trial stratification, and precision medicine.
