Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 7; pp. 3633 - 3644
• Main Authors: Chen, Oliver Y., Lipsmeier, Florian, Phan, Huy, Dondelinger, Frank, Creagh, Andrew, Gossens, Christian, Lindemann, Michael, de Vos, Maarten
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Assessments; Autoimmune diseases; Balance; Biomedical measurement; Customization; digital health technology; Ensemble learning; Extremities; Gait; generalized estimation equation; Humans; longitudinal prediction; Mathematical models; Missing data; missing data imputation; Multiple sclerosis; Multiple Sclerosis - diagnosis; Prediction models; Predictive models; Pulse width modulation; Remote sensors; Sensors; Smart phones; Smartphone; smartphone sensors; Smartphones; subject-specific fine-tuning
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3272117

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using smartphone sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.