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Fisher, Charles K.; Smith, Aaron M.; Walsh, Jonathan R.; Simon, Adam J.; Edgar, Chris; Jack, Clifford R.; Holtzman, David; Russell, David; Hill, Derek; Grosset, Donald; Wood, Fred; Vanderstichele, Hugo; Morris, John; Blennow, Kaj; Marek, Ken; Shaw, Leslie M; Albert, Marilyn; Weiner, Michael; Fox, Nick; Aisen, Paul; Cole, Patricia E.; Petersen, Ronald; Sherer, Todd; Kubick, Wayne

Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

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  • Media type: E-Article
  • Title: Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
  • Contributor: Fisher, Charles K.; Smith, Aaron M.; Walsh, Jonathan R.; Simon, Adam J.; Edgar, Chris; Jack, Clifford R.; Holtzman, David; Russell, David; Hill, Derek; Grosset, Donald; Wood, Fred; Vanderstichele, Hugo; Morris, John; Blennow, Kaj; Marek, Ken; Shaw, Leslie M; Albert, Marilyn; Weiner, Michael; Fox, Nick; Aisen, Paul; Cole, Patricia E.; Petersen, Ronald; Sherer, Todd; Kubick, Wayne
  • Published: Springer Science and Business Media LLC, 2019
  • Published in: Scientific Reports, 9 (2019) 1
  • Language: English
  • DOI: 10.1038/s41598-019-49656-2
  • ISSN: 2045-2322
  • Origination:
  • Footnote:
  • Description: AbstractMost approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.
  • Access State: Open Access