Natural language processing analysis of the theories of people with multiple sclerosis about causes of their disease.

ABSTRACT:

Background

While potential risk factors for multiple sclerosis (MS) have been extensively researched, it remains unclear how persons with MS theorize about their MS. Such theories may affect mental health and treatment adherence. Using natural language processing techniques, we investigated large-scale text data about theories that persons with MS have about the causes of their disease. We examined the topics into which their theories could be grouped and the prevalence of each theory topic.

Methods

A total of 486 participants of the Swiss MS Registry longitudinal citizen science project provided text data on their theories about the etiology of MS. We used the transformer-based BERTopic Python library for topic modeling to identify underlying topics. We then conducted an in

SUBMITTER: Haag C 

PROVIDER: S-EPMC11196672 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature