Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Jing Zhang

Frontiers in Neurology 2017 article cited by 32

importance 6/10 ai assisted Cerebral Palsy and Movement Disorders pediatric neurology stage: research

Early diagnosis of cerebral palsy in high-risk infants, plus identification of risk factors, movement impairment, and surgical outcome prediction.

Early CP detection enables early intervention and possible recovery in children with severe physical disability.

Review of multivariate analytic and machine learning studies used for CP risk factor identification, CP detection, movement assessment, and outcome prediction.

not stated in source

ML shown useful for automatically identifying movement impairments in high-risk infants, but clinical use requires large-sample validation.

Venue Frontiers in Neurology
Published 2017
Work type article
Authors Jing Zhang
Citations 32 (OpenAlex, at import time)
References 85
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.3389/fneur.2017.00715
Open access gold
Keywords cerebral palsy, machine learning, early diagnosis, multivariate analysis, movement assessment
6 / 10

significant — thousands to millions affected

Abstract states CP is a common pediatric movement disorder causing the most severe physical disability in children; early diagnosis enables intervention and recovery.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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