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

Presentation Time: 09:00 AM - 09:15 AM

Abstract Keywords: Ubiquitous Computing and Sensors, Machine Learning, Personal Health Informatics, Patient / Person Generated Health Data (Patient Reported Outcomes)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Clinical tools for measuring disease progression in amyotrophic lateral sclerosis (ALS) rely on in-clinic assessment, thus limiting the frequency of measurement and potentially delaying needed treatments. The ALS Functional Rating Scale Revised (ALSFRS-R) instrument, the gold standard for quantifying disease progression in ALS patients, can be subjective and does not capture day-to-day variability in function. As such, clinicians may be missing subtle yet criti- cal shifts in patient health status pointing to the need for more objective and continuous monitoring methods. In-home sensor technologies could supplement traditional clinical instruments with more frequent and quantitative measure- ments as early indicators of changes in function. This study evaluates the methodologies for integrating clinician scored scales obtained at one-month intervals with daily sensor-based health parameter estimates for building predic- tive models using participant case study data. Using the XGBoost regressor estimator in single base learning, we test the usability of interpolation on low frequency monthly ALSFRS-R assessments to align with high frequency sensor data features. Model error rates are evaluated to determine the suitability of sensor-based features as predictors for estimating component and composite scores. We find a mean RMSE of 0.276 across 9 ALSFRS-R sub-scale predictive models and an RMSE of 2.984 for predicting the composite ALSFRS-R score. Within the 10 models, models fit with interpolated assessment scores with lowest RMSE were represented by backward fill (3), exponential (3), sigmoid (1), inverse exponential (1), and cubic spline (1) interpolation types.

Speaker(s):
Noah Marchal, M.S.
University of Missouri

Author(s):
Noah Marchal, M.S. - University of Missouri; William Janes, OTD, MSCI, OTR/L - University of Missouri; Juliana Earwood, OTD, OTR/L - University of Missouri; Abu Mosa, PhD, MS, FAMIA - University of Missouri School of Medicine; Mihail Popescu, PhD - University of Missouri; Marjorie Skubic, PhD - University of Missouri; Xing Song, PhD - University of Missouri;