用家庭传感器数据半监督预测渐冻症病情进展，提升监测精度。
Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring
- 基于家庭传感器数据，用半监督模型估计ALSFRS-R评分变化趋势。
- 自注意力插值法在28个子项中表现最优，平均误差仅0.19。
- 按功能域特性选择模型：个性化适应适合呼吸/言语，迁移学习适合吞咽/穿衣。
ALS患者的功能衰退依赖周期性评估，易遗漏两次就诊间的动态变化。为此，研究构建了半监督回归模型，利用连续家庭传感器监测数据估计病例队列中ALSFRS-R量表轨迹的变化率。比较三种模型范式（个体批量学习、队列级批量与增量微调的迁移学习）在线性斜率、三次多项式及集成自注意力伪标签插值下的表现。结果表明，队列层面在各功能域具同质性，迁移学习在32个对比中的28个子项上降低预测误差（均方根误差均值0.20±0.04），个体批量学习在复合量表预测中表现更优（均值RMSE=3.15±1.25，3组中2组胜出）。自注意力插值在子项模型中误差最低（均值RMSE=0.19±0.06），能捕捉复杂非线性进展，在32组中20组优于线性和三次多项式；而线性插值在所有复合量表模型中表现最稳定（均值RMSE=0.23±0.10）。研究识别出不同功能域的异质性特征：呼吸与言语呈患者特异性模式，宜采用个性化增量适配；吞咽与穿衣则符合队列级轨迹，适合迁移学习。结论指出，依据功能域的同质-异质特性匹配学习与伪标签方法，可显著提升病情预测准确性。将自适应模型选择整合进传感器监测平台，有望实现及时干预和多中心研究的规模化部署。
原文摘要 · Abstract (English)
Clinical monitoring of functional decline in ALS relies on periodic assessments that may miss critical changes occurring between visits. To address this gap, semi-supervised regression models were developed to estimate rates of decline in a case series cohort by targeting ALSFRS- R scale trajectories with continuous in-home sensor monitoring data. Our analysis compared three model paradigms (individual batch learning and cohort-level batch versus incremental fine-tuned transfer learning) across linear slope, cubic polynomial, and ensembled self-attention pseudo-label interpolations. Results revealed cohort homogeneity across functional domains responding to learning methods, with transfer learning improving prediction error for ALSFRS-R subscales in 28 of 32 contrasts (mean RMSE=0.20(0.04)), and individual batch learning for predicting the composite scale (mean RMSE=3.15(1.25)) in 2 of 3. Self-attention interpolation achieved the lowest prediction error for subscale-level models (mean RMSE=0.19(0.06)), capturing complex nonlinear progression patterns, outperforming linear and cubic interpolations in 20 of 32 contrasts, though linear interpolation proved more stable in all ALSFRS-R composite scale models (mean RMSE=0.23(0.10)). We identified distinct homogeneity-heterogeneity profiles across functional domains with respiratory and speech exhibiting patient-specific patterns benefiting from personalized incremental adaptation, while swallowing and dressing functions followed cohort-level trajectories suitable for transfer models. These findings suggest that matching learning and pseudo-labeling techniques to functional domain-specific homogeneity-heterogeneity profiles enhances predictive accuracy in ALS progression tracking. Integrating adaptive model selection within sensor monitoring platforms could enable timely interventions and scalable deployment in future multi-center studies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。