A machine learning approach for predicting post-stroke aphasia recovery: A pilot study

Yiwen Gu, Murtadha Bahrani, Anne Billot, Sha Lai, Emily J. Braun, Maria Varkanitsa, Julia Bighetto, Brenda Rapp, Todd B. Parrish, David Caplan, Cynthia K. Thompson, Swathi Kiran, Margrit Betke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The potential recovery of post-stroke aphasia is highly variable and the rehabilitation outcomes are difficult to predict. This interdisciplinary collaboration builds on data collected as part of a large set of behavioral and brain variables in patients with post-stroke aphasia, charting the course of recovery associated with therapy across language domains and examining the basis of neuroplasticity. In this pilot study, we created and tested a predictive framework based on a subset of the data collected and developed machine-learning algorithms that take as input a complex set of brain and behavioral features to classify and predict the participants' responsiveness to therapy. We developed Random Forest models that enabled us to rank the importance of these features. We then compared the contributions of different feature sets and discussed their physiological implications. Our preliminary results suggest the potential of our framework, and, thus, this study takes an important first step towards predicting individualized rehabilitation outcomes.

Original languageEnglish (US)
Title of host publication13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2020 - Conference Proceedings
PublisherAssociation for Computing Machinery
Pages161-169
Number of pages9
ISBN (Electronic)9781450377737
DOIs
StatePublished - Jun 30 2020
Event13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2020 - Virtual, Online, Greece
Duration: Jun 30 2020Jul 3 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2020
CountryGreece
CityVirtual, Online
Period6/30/207/3/20

Keywords

  • aphasia
  • machine learning
  • recovery
  • stroke

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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