Identifying treatment non-responders based on pre-treatment gait characteristics - A machine learning approach.

ABSTRACT:

Background

Paediatric movement disorders such as cerebral palsy often negatively impact walking behaviour. Although clinical gait analysis is usually performed to guide therapy decisions, not all respond positively to their assigned treatment. Identifying these individuals based on their pre-treatment characteristics could guide clinicians towards more appropriate and personalized interventions. Using routinely collected pre-treatment gait and anthropometric features, we aimed to assess whether standard machine learning approaches can be effective in identifying patients at risk of negative treatment outcomes.

Methods

Observational data of 119 patients with movement disorders were retrospectively extracted from a local clinical database, comprising sagittal joint angles and

SUBMITTER: Visscher RMS 

PROVIDER: S-EPMC10613900 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature