Back to search

Article

Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity

2024-09-18

Abstract excerpt

<h4>Importance</h4> Declining mortality in the field of pediatric critical care medicine has shifted practicing clinicians’ attention to preserving patients’ neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and...

Topics

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

Identifiers and source

Literature Corpus work
984a2e11-9760-5fb0-a9b2-132ea002c67f
DOI
10.1101/2024.09.17.24313649
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.
Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic MorbidityDOI 10.1101/2024.09.17.24313649
Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenSemantic proximity
Developing and Validating a Machine Learning Model to Predict Brain Injury in Preterm Infants Using Multisource Data from the Early Postnatal PeriodSemantic 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
Early Prediction of Acute Kidney Injury in Pediatric Intensive Care Unit Patients Using an XGBoost-Based Machine Learning Model: A Retrospective StudySemantic proximity
Multicenter Validation of a Machine Learning Algorithm for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki DiseaseSemantic proximity
A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER programSemantic proximity
Reengineering a machine learning phenotype to adapt to the changing COVID-19 landscape: A study from the N3C and RECOVER consortiaSemantic proximity
Design and applicability of clinical prediction models for serious infection in children with fever: a scoping review using causal directed acyclic graphsSemantic proximity
Non-Invasive Cerebral Edema Monitoring and High-Risk Blood Biomarkers in Pediatric Sepsis-Associated Encephalopathy: A Clinical Application StudySemantic proximity
A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.Semantic proximity
Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case StudySemantic proximity
Geographical validation of the Smart Triage Model by age groupSemantic proximity
Select a neighboring publication to make it the new centre.