Triage Prediction of a Real Dataset of COVID-19 Patients in Alava

  1. Goizalde Badiola-Zabala 1
  2. Jose Manuel Lopez-Guede 1
  3. Julian Estevez 2
  4. Manuel Graña 2
  1. 1 University (UPV/EHU), Vitoria-Gasteiz, Spain
  2. 2 University (UPV/EHU) Donostia-San Sebastian, Spain
Libro:
Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Alvarez Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Hojjat Adeli

Editorial: Springer Suiza

ISBN: 978-3-031-06527-9

Ano de publicación: 2022

Páxinas: 472-481

Tipo: Capítulo de libro

Resumo

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.