Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools.

Saved in:
Bibliographic Details
Title: Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools.
Authors: Yu M; Telemedicine Program, Children's National Hospital, Washington, DC., Saenz Gaitan L; Telemedicine Program, Children's National Hospital, Washington, DC.; Department of Pediatrics, The George Washington University, School of Medicine and Health Sciences, Washington, DC., Lopez Magallon A; Telemedicine Program, Children's National Hospital, Washington, DC.; Department of Pediatrics, The George Washington University, School of Medicine and Health Sciences, Washington, DC.; Division of Cardiac Critical Care, Children's National Hospital, Washington, DC., Futterman C; Department of Pediatrics, The George Washington University, School of Medicine and Health Sciences, Washington, DC.; Division of Cardiac Critical Care, Children's National Hospital, Washington, DC., Jin F; Department of Statistics, The George Washington University, School of Medicine and Health Sciences, Washington, DC., Linguraru MG; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC.; Department of Radiology, The George Washington University, School of Medicine and Health Sciences, Washington, DC., Anwar SM; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC.; Department of Radiology, The George Washington University, School of Medicine and Health Sciences, Washington, DC., Munoz R; Telemedicine Program, Children's National Hospital, Washington, DC.; Department of Pediatrics, The George Washington University, School of Medicine and Health Sciences, Washington, DC.; Division of Cardiac Critical Care, Children's National Hospital, Washington, DC.
Source: Critical care explorations [Crit Care Explor] 2026 Feb 09; Vol. 8 (2), pp. e1381. Date of Electronic Publication: 2026 Feb 09 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Wolters Kluwer Health Country of Publication: United States NLM ID: 101746347 Publication Model: eCollection Cited Medium: Internet ISSN: 2639-8028 (Electronic) Linking ISSN: 26398028 NLM ISO Abbreviation: Crit Care Explor Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2639-8028
DOI:10.1097/CCE.0000000000001381