Marchal Noah, Janes William E, Earwood Juliana H, Mosa Abu S M, Popescu Mihail, Skubic Marjorie, Song Xing
Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA.
Department of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri, Columbia, Missouri, USA.
AMIA Annu Symp Proc. 2025 May 22;2024:788-797. eCollection 2024.
Clinical tools for tracking functional decline in amyotrophic lateral sclerosis (ALS) rely on in-clinic guided assessments, such as the gold standard ALS Functional Rating Scale Revised (ALSFRS-R) instrument, thus limiting the frequency of collection and potentially delaying needed treatments. As such, ALS clinicians may miss subtle yet critical shifts inpatient health -pointing to the needfor objective and continuous capturing of day-to-day functional status. In-home health sensors could supplement clinical instruments with more frequent, quantitative measurements as early indicators of change. Using the XGBoost regressor in base learning, we explore interpolation techniques for aligning monthly ALSFRS-R assessment targets with high frequency sensor-based health features. We evaluated 9 interpolation models, which demonstrate superior prediction of ALSFRS-R scores compared to traditional clinical scale estimates based on linear slope. This pilot work provides a practical approach of modeling mixed-frequency data and shows the potential of using sensor-based health estimates as sensitive prognostic markers.
用于追踪肌萎缩侧索硬化症（ALS）功能衰退的临床工具依赖于临床指导评估，如金标准修订版ALS功能评定量表（ALSFRS-R），因此限制了数据收集的频率，并可能延迟所需治疗。正因如此，ALS临床医生可能会错过患者健康状况中细微但关键的变化，这表明需要客观且持续地获取日常功能状态。家庭健康传感器可以通过更频繁的定量测量来补充临床仪器，作为变化的早期指标。在基础学习中使用XGBoost回归器，我们探索了插值技术，以使每月的ALSFRS-R评估目标与基于高频传感器的健康特征相匹配。我们评估了9种插值模型，与基于线性斜率的传统临床量表估计相比，这些模型对ALSFRS-R分数的预测更优。这项试点工作提供了一种对混合频率数据进行建模的实用方法，并展示了将基于传感器的健康估计用作敏感预后标志物的潜力。
