Integrating Multi-sensor Time-series Data for ALSFRS-R Clinical Scale Predictions in an ALS Patient Case Study

Abstract

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.