Personalized Longitudinal Assessment of Multiple Sclerosis Using
Smartphones
- URL: http://arxiv.org/abs/2209.09692v1
- Date: Tue, 20 Sep 2022 12:56:29 GMT
- Title: Personalized Longitudinal Assessment of Multiple Sclerosis Using
Smartphones
- Authors: Oliver Y. Ch\'en, Florian Lipsmeier, Huy Phan, Frank Dondelinger,
Andrew Creagh, Christian Gossens, Michael Lindemann, Maarten de Vos
- Abstract summary: We design a novel longitudinal model to map individual disease trajectories using sensor data that may contain missing values.
parameters learned from multiple training datasets are ensembled to form a simple, unified predictive model.
The results show that the proposed model is promising to achieve personalized longitudinal MS assessment.
- Score: 9.186241234772702
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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