# Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones

arXiv: https://arxiv.org/abs/2209.09692 · HTML: https://mlsanity.com/paper/2209.09692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Disability prediction in multiple sclerosis using performance outcome measures and demographic data](https://mlsanity.com/paper/2204.03969/) (2022)
- [New Confidence Intervals and Bias Comparisons Show that Maximum Likelihood Can Beat Multiple Imputation in Small Samples](https://mlsanity.com/paper/1307.5875/) (2013)

## Built on by (most cited first, 2 of 2)

- [A State-of-the-Art Review of Computational Models for Analyzing Longitudinal Wearable Sensor Data in Healthcare](https://mlsanity.com/paper/2407.21665/) (2024)
- [OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values](https://mlsanity.com/paper/2503.11282/) (2025)

## Abstract (arXiv)

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.