Data-driven identification of metabolic and cardiovascular biomarkers in high-altitude workers: a machine learning approach
ML models can effectively identify critical health indicators related to FFW in high-altitude environments. These tools offer the potential to improve occupational health assessments and support preventive decision making in vulnerable worker populations.
Authors
- Gonzalo Blanco (ORCID: https://orcid.org/0000-0003-3827-3111)
- Guillermo Droppelmann (ORCID: https://orcid.org/0000-0001-9164-1150)
- Felipe Feijoo (ORCID: https://orcid.org/0000-0003-3129-9597)
- Max Dollmann
- Ricardo Jorquera
- Ignacio Ahumada
Institutions
- Pontificia Universidad Católica de Valparaíso (CL)
- Instituto de Radiomedicina (CL)
- Clínica MEDS (Chile) (CL)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-11-28
- DOI
- https://doi.org/10.3389/fpubh.2025.1652605
- Primary Topic
- High Altitude and Hypoxia
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Pontificia Universidad Católica de Valparaíso