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Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have bee...

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Autor principal: Zhang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5742591/
https://www.ncbi.nlm.nih.gov/pubmed/29312134
http://dx.doi.org/10.3389/fneur.2017.00715
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author Zhang, Jing
author_facet Zhang, Jing
author_sort Zhang, Jing
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description Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
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spelling pubmed-57425912018-01-08 Multivariate Analysis and Machine Learning in Cerebral Palsy Research Zhang, Jing Front Neurol Neuroscience Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. Frontiers Media S.A. 2017-12-21 /pmc/articles/PMC5742591/ /pubmed/29312134 http://dx.doi.org/10.3389/fneur.2017.00715 Text en Copyright © 2017 Zhang. http://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) or licensor 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 Neuroscience
Zhang, Jing
Multivariate Analysis and Machine Learning in Cerebral Palsy Research
title Multivariate Analysis and Machine Learning in Cerebral Palsy Research
title_full Multivariate Analysis and Machine Learning in Cerebral Palsy Research
title_fullStr Multivariate Analysis and Machine Learning in Cerebral Palsy Research
title_full_unstemmed Multivariate Analysis and Machine Learning in Cerebral Palsy Research
title_short Multivariate Analysis and Machine Learning in Cerebral Palsy Research
title_sort multivariate analysis and machine learning in cerebral palsy research
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5742591/
https://www.ncbi.nlm.nih.gov/pubmed/29312134
http://dx.doi.org/10.3389/fneur.2017.00715
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