Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools
A ML model can predict and discriminate between several types of AEs in SV infants before bidirectional Glenn. Accurate predictions may help perform timely interventions, potentially reducing morbidity, mortality, and healthcare costs.
Authors
- Alejandro Lopez Magallon (ORCID: https://orcid.org/0000-0002-1785-6011)
- Craig Futterman (ORCID: https://orcid.org/0000-0003-3093-5432)
- Syed Muhammad Anwar
- Min Yu
- Marius George Linguraru
- Lucas Saenz Gaitan
- Fang Jin
- Ricardo Munoz
Institutions
- Children's National (US)
- George Washington University (US)
Publication Details
- Journal
- Critical Care Explorations
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1097/cce.0000000000001381
- Primary Topic
- Phonocardiography and Auscultation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00