Multivariate Analysis and Machine Learning in Cerebral Palsy Research - Zhang, Jing (Frontiers in Neurology, 2017)
Cerebral palsy (CP) is the most common 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 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. The 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 machine learning 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 machine learning approaches useful in the 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 analytic approaches and data processing technologies advance in the Big Data era of the 21st century, it is anticipated that multivariate analysis and machine learning will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rate, and enhance patient care for children with CP.