Triage Prediction of a Real Dataset of COVID-19 Patients in Alava
- Goizalde Badiola-Zabala 1
- Jose Manuel Lopez-Guede 1
- Julian Estevez 2
- Manuel Graña 2
- 1 University (UPV/EHU), Vitoria-Gasteiz, Spain
- 2 University (UPV/EHU) Donostia-San Sebastian, Spain
- José Manuel Ferrández Vicente (dir. congr.)
- José Ramón Alvarez Sánchez (dir. congr.)
- Félix de la Paz López (dir. congr.)
- Hojjat Adeli
Editorial: Springer Suiza
ISBN: 978-3-031-06527-9
Año de publicación: 2022
Páginas: 472-481
Tipo: Capítulo de Libro
Resumen
The COVID-19 pandemic has increased the pressure on developing clinical decision-making systems based on predictive algorithms, potentially helping to reduce the unmanageable strain on healthcare systems. In an attempt to address this challenging health situation, we attempted to provide a contribution to this endeavour with an in-depth study of a real-life dataset of covid-19 patients from a local hospital. In this paper, we approach the problem as triage prediction problem, formulated as multi-class classification problem, with special care on the age normalization of physiological variables. We report experimental results obtained on a data sample covering COVID-19 patients assisted in a local hospital. To do this, we tried to emulate the triage decisions of the physicians recorded in a dataset containing the measurements of physiological variables and the triage decision. We obtained results that provide encouragement for a real-life application development of the data balancing and classification in the prediction of the triage that the medical doctors will assign the critical patients.