Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones

TL;DR

This paper presents a novel smartphone-based longitudinal model for personalized multiple sclerosis assessment, leveraging sensor data on gait, balance, and upper extremity functions, with methods to handle missing data and subject-specific tuning.

Contribution

It introduces a new automated longitudinal modeling approach for MS using sensor data, including missing data imputation and personalized prediction enhancements.

Findings

The model accurately forecasts MS progression in unseen individuals.

Sensor features related to gait, balance, and upper extremity functions are effective digital markers.

Personalized fine-tuning improves prediction accuracy for severe cases.

Abstract

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 the idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using 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.