Article

Machine learning to predict outcomes of fetal cardiac disease: a pilot study

2024-03-13

Abstract excerpt

<title>Abstract</title> <p><bold>BACKGROUND:</bold> Prediction of outcomes following a prenatal diagnosis of congenital heart disease is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. <bold>METHODS:</bold> We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to pre...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bc3d228c-05bc-588d-905c-695c1b9757ae
DOI: 10.21203/rs.3.rs-4045996/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning to predict outcomes of fetal cardiac disease: a pilot studyDOI 10.21203/rs.3.rs-4045996/v1Read

Chromosomal analysis and short-term outcome of prenatally diagnosed complex congenital heart diseaseSemantic proximity Read

Developing and Validating a Machine Learning Model to Predict Brain Injury in Preterm Infants Using Multisource Data from the Early Postnatal PeriodSemantic proximity Read

Unveiling the unborn: Advancing fetal health classification through machine learningSemantic proximity Read

Prognostic Impact of HFpEF in Hypertrophic Cardiomyopathy: A Machine Learning-Based Risk Stratification StudySemantic proximity Read

Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised LearningSemantic proximity Read

Genetic, demographic and clinical variables act synergistically to impact neurodevelopmental outcomes in children with single ventricle heart diseaseSemantic proximity Read

Genome Sequencing is Critical for Forecasting Outcomes Following Congenital Cardiac SurgerySemantic proximity Read

A machine learning model for prediction of early-onset neonatal sepsis in low- and middle-income countries: Development and validation studySemantic proximity Read

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Semantic proximity Read

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.Semantic proximity Read

A retrospective analysis of fetal heart defects in China: Investigating the influencing factors of pregnancy outcomes and evaluating the value of the multidisciplinary team cooperation approachSemantic proximity Read

AI vs. Traditional ultrasound study in Congenital Heart Defect Detection: A Systematic reviewSemantic proximity Read