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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Pain Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpain.2022.859881/full |
| _version_ | 1818214518764863488 |
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| author | Scott Holmes Scott Holmes Joud Mar'i Laura E. Simons David Zurakowski Alyssa Ann LeBel Michael O'Brien David Borsook |
| author_facet | Scott Holmes Scott Holmes Joud Mar'i Laura E. Simons David Zurakowski Alyssa Ann LeBel Michael O'Brien David Borsook |
| author_sort | Scott Holmes |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-12T06:21:28Z |
| format | Article |
| id | doaj.art-f17ef81e17e24b1f82599a986ac22334 |
| institution | Directory Open Access Journal |
| issn | 2673-561X |
| language | English |
| last_indexed | 2024-12-12T06:21:28Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pain Research |
| spelling | doaj.art-f17ef81e17e24b1f82599a986ac223342022-12-22T00:34:54ZengFrontiers Media S.A.Frontiers in Pain Research2673-561X2022-05-01310.3389/fpain.2022.859881859881Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy ControlsScott Holmes0Scott Holmes1Joud Mar'i2Laura E. Simons3David Zurakowski4Alyssa Ann LeBel5Michael O'Brien6David Borsook7Pediatric Pain Pathway Lab, Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital – Harvard Medical School, Boston, MA, United StatesPain and Affective Neuroscience Center, Boston Children's Hospital, Boston, MA, United StatesPediatric Pain Pathway Lab, Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital – Harvard Medical School, Boston, MA, United StatesDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United StatesDepartment of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United StatesDepartment of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United StatesSports Medicine Division, Sports Concussion Clinic, Orthopedic Surgery, Harvard Medical School, Boston, MA, United StatesDepartments of Psychiatry ad Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United StatesPost-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.https://www.frontiersin.org/articles/10.3389/fpain.2022.859881/fullmachine learningMRIpost-traumatic headachepainpediatrics |
| spellingShingle | Scott Holmes Scott Holmes Joud Mar'i Laura E. Simons David Zurakowski Alyssa Ann LeBel Michael O'Brien David Borsook Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls Frontiers in Pain Research machine learning MRI post-traumatic headache pain pediatrics |
| 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 | machine learning MRI post-traumatic headache pain pediatrics |
| url | https://www.frontiersin.org/articles/10.3389/fpain.2022.859881/full |
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