A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
Keywords:
neonatal seizures, risk factors, epilepsy, mortality, logistic regression, decision treeAbstract
There is a controversial concept among many studies whether neonatal seizures are risk factors for neonatal death and/or neurodevelopment impairments (in case of newborn survivors). Multiple factors have been analyzed in literature, including perinatal factors, etiology factors, seizures characteristics factors, investigations findings factors, therapy-related factors. This paper aims to review the characteristics and the application context of different computational models developed for identifying both the risk factor of morbidity (epilepsy, cerebral palsy, development disability or their combination) and the mortality outcome after neonatal seizures. Consequently, we determined the groups of main risk factors using factor analysis. The vast majority of identified models are logistic regression models, but also decision tree models. In the literature, there is a large variation in establishing the risk factors determining poor or favorable outcome after a neonatal seizure, with similarities and inconsistencies. These findings could be a consequence of different approaches regarding inclusion criteria, methodologies used to identify seizure, seizures definition or description, analysis using computational models.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant this journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work, with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as an earlier and greater citation of published work (See The Effect of Open Access).
BRAIN. Broad Research in Artificial Intelligence and Neuroscience Journal has an Attribution-NonCommercial-NoDerivs
CC BY-NC-ND