@inproceedings{378540672c824ef1a1567c214d0cc34b,
title = "A machine learning approach for predicting post-stroke aphasia recovery: A pilot study",
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.",
keywords = "aphasia, machine learning, recovery, stroke",
author = "Yiwen Gu and Murtadha Bahrani and Anne Billot and Sha Lai and Braun, {Emily J.} and Maria Varkanitsa and Julia Bighetto and Brenda Rapp and Parrish, {Todd B.} and David Caplan and Thompson, {Cynthia K.} and Swathi Kiran and Margrit Betke",
note = "Funding Information: We would like to thank all the individuals with aphasia who participated in this study and their families. We also acknowledge the work of our collaborators through the Center for the Neurobiology of Language Recovery. This work was supported by the NIH/NIDCD Clinical Research Center Grant, P50DC012283, the Hariri Institute Artificial Intelligence Research (AIR) Initiative and the Institute for Health System Innovation & Policy (IHSIP) at Boston University. Publisher Copyright: {\textcopyright} 2020 ACM.; 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2020 ; Conference date: 30-06-2020 Through 03-07-2020",
year = "2020",
month = jun,
day = "30",
doi = "10.1145/3389189.3389204",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "161--169",
booktitle = "13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2020 - Conference Proceedings",
}