Artificial intelligence-driven clustering for phenotyping life-threatening prehospital trauma

The AI method identified three clusters with implications for therapy and outcomes. This novel approach could help emergency medical services characterize trauma patients by providing benefits, treatment and resource optimization.

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Publication Details

Journal
Scandinavian Journal of Trauma Resuscitation and Emergency Medicine
Published
2026-01-15
DOI
https://doi.org/10.1186/s13049-026-01553-0
Primary Topic
Trauma and Emergency Care Studies
Type
article
Field-Weighted Citation Impact
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article

Artificial intelligence-driven clustering for phenotyping life-threatening prehospital trauma

Carlos del Pozo Vegas, Erik López Alonso, Raul Lopez-Izquierdo, Begoña Polonio-López et al.
Scandinavian Journal of Trauma Resuscitation and Emergency Medicine
Trauma and Emergency Care Studies
article

Artificial intelligence-driven clustering for phenotyping life-threatening prehospital trauma

Carlos del Pozo Vegas, Erik López Alonso, Raul Lopez-Izquierdo, Begoña Polonio-López, Ancor Sanz-García, José Luis Martin-Conty, Asier Losada, Francisco Martín-Rodríguez, Mikel Idoyaga, Rubén Pérez-García
article en

Abstract

The AI method identified three clusters with implications for therapy and outcomes. This novel approach could help emergency medical services characterize trauma patients by providing benefits, treatment and resource optimization.

Scandinavian Journal of Trauma Resuscitation and Emergency MedicineVol. 34(1)
Universidad de Valladolid (ES), University of the Basque Country (ES), Instituto de Salud Carlos III (ES), BioCruces Health research Institute (ES), Osakidetza (ES), Servicio de Salud de Castilla La Mancha (ES), Hospital Clínico Universitario de Valladolid (ES), Hospital Universitario Río Hortega (ES), University of Castilla-La Mancha (ES)
Good health and well-being
Openalex Percentile: Top 89%
Trauma and Emergency Care Studies
DOIOpen Access PDFOpenAlex
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