Enhancing tertiary cardiology triage with vectorcardiographic features: a machine learning approach using real-world data
VCG-derived features may improve the identification of patients requiring tertiary care, either alone or integrated into an explainable and robust machine learning model trained on real-world data. Its clinical value will ultimately depend on prospective validation and seamless integration within existing care pathways.
Authors
- Renata Valeri de Freitas (ORCID: https://orcid.org/0000-0001-5029-6231)
- Vinicius Ruiz Uemoto (ORCID: https://orcid.org/0000-0002-9629-4298)
- Renato de Aguiar Hortegal
- Lucas José da Costa
- Mariana FN de Marchi
Institutions
- Universidade de São Paulo (BR)
- Instituto Nacional de Ciência e Tecnologia de Neurociência Translacional (BR)
- Instituto Dante Pazzanese de Cardiologia (BR)
Publication Details
- Journal
- Clinics
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1016/j.clinsp.2025.100856
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00