Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.

Anat MirelmanMor Ben Or FrankMichal MelamedLena GranovskyAlice NieuwboerLynn RochesterSilvia Del DinLaura AvanzinoElisa PelosinBastiaan R BloemUgo Della CroceAndrea CereattiPaolo BonatoRichard CamicioliTheresa EllisJamie L HamiltonChris J HassQuincy J AlmeidaMaidan InbalAvner ThalerJulia ShirvanJesse M CedarbaumNir GiladiJeffrey M Hausdorff

Published in: Movement disorders : official journal of the Movement Disorder Society (2021)

Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

Keyphrases

machine learning
clinical trial
artificial intelligence
big data
deep learning
heart rate
randomized controlled trial
blood pressure
cerebral palsy
open label
double blind