Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

Journal: BMJ paediatrics open

PMID: 39214549

Abstract

OBJECTIVE: Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aimed to identify the early predictors of CP using machine learning (ML).

Authors

Sara Rapuc

Department of Pediatric Neurology, University Children's Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia.
Blaž Stres

Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Ljubljana, Slovenia.
Ivan Verdenik

Department of Perinatology, Division of Obstetrics and Gynecology, University Medical Centre Ljubljana, Ljubljana, Slovenia.
Miha Lučovnik

Department of Perinatology, Division of Obstetrics and Gynecology, University Medical Centre Ljubljana, Ljubljana, Slovenia.
Damjan Osredkar

Department of Pediatric Neurology, University Children's Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia damjan.osredkar@kclj.si.

Keywords

Case-Control Studies Cerebral Palsy Female Humans Infant, Newborn Machine Learning Male Pregnancy Retrospective Studies Risk Factors ROC Curve Sensitivity and Specificity Slovenia

External Resources

View on PubMed Access via DOI PubMed (39214549)

Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals