Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage.

ABSTRACT: Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerf

SUBMITTER: Vignolle GA 

PROVIDER: S-EPMC11477204 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature