Neural networks to estimate multiple sclerosis disability and predict progression using routinely collected healthcare data.

ABSTRACT:

Background and objectives

Multiple sclerosis (MS)-related disability is conventionally measured using the Expanded Disability Status Scale (EDSS), which requires neurological examination and is generally embedded in clinical records, making it unavailable in administrative datasets. This limits its utility for population-level estimates and healthcare planning. This study aims to use routinely collected healthcare data to fill this gap.

Methods

We conducted a population-based study using administrative data from Campania Region (Italy) to develop and validate neural network algorithms to estimate MS-related disability and predict its progression (2015-2021). We employed a deep learning approach to estimate the EDSS, and a hybrid model combining survival analysis with neural

SUBMITTER: Affinito G 

PROVIDER: S-EPMC12547042 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature