Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning.

Journal: The Lancet. Digital health
PMID: 34334334

Abstract

BACKGROUND: Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson's disease progression assumed static progression trajectories within subgroups and have not adequately accounted for complex medication effects. Our objective was to develop a statistical progression model of Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects.

Authors

  • Kristen A Severson
    Center for Computational Health, IBM Research, Cambridge, MA, USA. Electronic address: kristen.severson@ibm.com.
  • Lana M Chahine
    Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
  • Luba A Smolensky
    Michael J Fox Foundation, New York, NY, USA.
  • Murtaza Dhuliawala
    Center for Computational Health, IBM Research, Cambridge, MA, USA.
  • Mark Frasier
    Michael J Fox Foundation, New York, NY, USA.
  • Kenney Ng
    Center for Computational Health, IBM Research, Yorktown Heights, NY, USA.
  • Soumya Ghosh
    Department of Biological Sciences, Thompson Rivers University, Kamloops, BC V2C 0C8, Canada. sghosh@tru.ca.
  • Jianying Hu
    Center for Computational Health, IBM Research, Yorktown Heights, NY, USA.

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