Development and validation of a machine learning model for accurate detection of wrong blood in tube errors in hospitalized patients
This machine-learning model, leveraging widely available routine laboratory parameters, shows strong potential for integration into clinical workflows, enhancing WBIT detection and improving patient safety.
Authors
- Antonio Sierra‐Rivera (ORCID: https://orcid.org/0000-0001-7824-7239)
- Óscar Fuster (ORCID: https://orcid.org/0000-0001-9011-2062)
- Laura Sahuquillo Frías (ORCID: https://orcid.org/0000-0003-2975-8862)
- Jordi Tortosa-Carreres (ORCID: https://orcid.org/0000-0002-4622-2173)
- Begoña Laiz-Marro (ORCID: https://orcid.org/0009-0003-1526-3575)
- Rafael Gisbert‐Criado (ORCID: https://orcid.org/0000-0003-4845-3706)
- Ana Comes-Raga (ORCID: https://orcid.org/0000-0002-8839-5080)
- Ana Vañó-Bellver (ORCID: https://orcid.org/0000-0002-5103-6447)
- Virginia Tadeo-Garisto
- Alexandra Igumnova-Dubovikova
- Andreu Martínez-Cerezuela (ORCID: https://orcid.org/0000-0003-3801-2181)
- Lucía García-Ruiz
- Elena Rodríguez-Romero
- Carlos Cátedra
Institutions
- Hospital Universitari i Politècnic La Fe (ES)
- Hospital Arnau de Vilanova (ES)
- Hospital General Universitario De Valencia (ES)
Publication Details
- Journal
- Clinical Chemistry and Laboratory Medicine (CCLM)
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1515/cclm-2025-0564
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00