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Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls

Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient exp...

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Autores principales: Holmes, Scott, Mar'i, Joud, Simons, Laura E., Zurakowski, David, LeBel, Alyssa Ann, O'Brien, Michael, Borsook, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9152124/
https://www.ncbi.nlm.nih.gov/pubmed/35655747
http://dx.doi.org/10.3389/fpain.2022.859881
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author Holmes, Scott
Mar'i, Joud
Simons, Laura E.
Zurakowski, David
LeBel, Alyssa Ann
O'Brien, Michael
Borsook, David
author_facet Holmes, Scott
Mar'i, Joud
Simons, Laura E.
Zurakowski, David
LeBel, Alyssa Ann
O'Brien, Michael
Borsook, David
author_sort Holmes, Scott
collection PubMed
description Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient experience with underlying biology when attempting to classify persons with PTH, particularly in the pediatric setting where patient self-report may be highly variable. The objective of this investigation was to evaluate the use of different machine learning (ML) classifiers to differentiate pediatric and young adult subjects with PTH from healthy controls using behavioral data from self-report questionnaires that reflect concussion symptoms, mental health, pain experience of the participants, and structural brain imaging from cortical and sub-cortical locations. Behavioral data, alongside brain imaging, survived data reduction methods and both contributed toward final models. Behavioral data that contributed towards the final model included both the child and parent perspective of the pain-experience. Brain imaging features produced two unique clusters that reflect regions that were previously found in mild traumatic brain injury (mTBI) and PTH. Affinity-based propagation analysis demonstrated that behavioral data remained independent relative to neuroimaging data that suggest there is a role for both behavioral and brain imaging data when attempting to classify children with PTH.
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spelling pubmed-91521242022-06-01 Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls Holmes, Scott Mar'i, Joud Simons, Laura E. Zurakowski, David LeBel, Alyssa Ann O'Brien, Michael Borsook, David Front Pain Res (Lausanne) Pain Research Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient experience with underlying biology when attempting to classify persons with PTH, particularly in the pediatric setting where patient self-report may be highly variable. The objective of this investigation was to evaluate the use of different machine learning (ML) classifiers to differentiate pediatric and young adult subjects with PTH from healthy controls using behavioral data from self-report questionnaires that reflect concussion symptoms, mental health, pain experience of the participants, and structural brain imaging from cortical and sub-cortical locations. Behavioral data, alongside brain imaging, survived data reduction methods and both contributed toward final models. Behavioral data that contributed towards the final model included both the child and parent perspective of the pain-experience. Brain imaging features produced two unique clusters that reflect regions that were previously found in mild traumatic brain injury (mTBI) and PTH. Affinity-based propagation analysis demonstrated that behavioral data remained independent relative to neuroimaging data that suggest there is a role for both behavioral and brain imaging data when attempting to classify children with PTH. Frontiers Media S.A. 2022-05-17 /pmc/articles/PMC9152124/ /pubmed/35655747 http://dx.doi.org/10.3389/fpain.2022.859881 Text en Copyright © 2022 Holmes, Mar'i, Simons, Zurakowski, LeBel, O'Brien and Borsook. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pain Research
Holmes, Scott
Mar'i, Joud
Simons, Laura E.
Zurakowski, David
LeBel, Alyssa Ann
O'Brien, Michael
Borsook, David
Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls
title Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls
title_full Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls
title_fullStr Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls
title_full_unstemmed Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls
title_short Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls
title_sort integrated features for optimizing machine learning classifiers of pediatric and young adults with a post-traumatic headache from healthy controls
topic Pain Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9152124/
https://www.ncbi.nlm.nih.gov/pubmed/35655747
http://dx.doi.org/10.3389/fpain.2022.859881
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