Back to search

Article

Explainable machine-learning predictions for complications after pediatric congenital heart surgery

2021-02-16

Abstract excerpt

The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of therapy and improve the quality of prognosis. Here, we develop an interpretable machine-learning-based model that integrates patient demographics, surgery-specific features...

Topics

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

Identifiers and source

Literature Corpus work
196b8be3-6131-5f48-b158-8f17003def22
DOI
10.21203/rs.3.rs-221270/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Explainable machine-learning predictions for complications after pediatric congenital heart surgeryDOI 10.21203/rs.3.rs-221270/v1
Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale studySemantic proximity
Intradialytic hypotension and hemodynamic phenotypes in children following continuous renal replacement therapy initiation.Semantic proximity
Development and Validation of a Risk Score for Predicting Stage 2 or 3 Acute Kidney Injury on Arrival to the Pediatric Cardiac Intensive Care Unit for Infants Undergoing Cardiac Surgery for Congenital Heart Disease.Semantic proximity
Value of preoperative risk adjustment in congenital heart surgery (RACHS) score versus the vasoactive-ventilation-renal (VVR) score for predicting postoperative outcomes in neonates and childrenSemantic proximity
Evaluation of risk factors affecting morbidity in patients who underwent surgical closure of ventricular septal defectSemantic proximity
Are Outcomes in Congenital Cardiac Surgery Better Than Ever?Semantic proximity
Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgerySemantic proximity
EXTERNAL VALIDATION OF THE IMPROVING PARTIAL RISK ADJUSTMENT IN SURGERY (PRAIS2) MODEL FOR 30-DAY MORTALITY AFTER PEDIATRIC CARDIAC SURGERYSemantic proximity
Genetic variation in the mitochondrial enzyme carbamyl-phosphate synthetase I predisposes children to increased pulmonary artery pressure following surgical repair of congenital heart defects: a validated genetic association study.Semantic proximity
Validation of Clinical Prediction Models with Repeated-Measures Predictors: A Methodological Framework for Neonatal Digital Twins in IoT EnvironmentsSemantic proximity
Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit (PICU)Semantic proximity
Neutrophil extracellular traps formation is associated with postoperative complications in neonates and infants undergoing congenital cardiac surgerySemantic proximity
Select a neighboring publication to make it the new centre.