Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones.

Details

Ressource 1Download: MS_Chén et al (2023).pdf (3211.62 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_E9CEFF2B9677
Type
Article: article from journal or magazin.
Collection
Publications
Title
Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones.
Journal
IEEE journal of biomedical and health informatics
Author(s)
Chén Oliver Y, Lipsmeier F., Phan H., Dondelinger F., Creagh A., Gossens C., Lindemann M., de Vos M.
ISSN
2168-2208 (Electronic)
ISSN-L
2168-2194
Publication state
Published
Issued date
07/2023
Peer-reviewed
Oui
Volume
27
Number
7
Pages
3633-3644
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
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 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.
Keywords
Humans, Multiple Sclerosis/diagnosis, Smartphone, Gait
Pubmed
Web of science
Open Access
Yes
Create date
11/01/2024 19:05
Last modification date
18/01/2024 16:20
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