Innovation Through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics

Overview

Journal Biomimetics (Basel)

Date 2024 Jul 26

PMID 39056881

Authors

Nicolas J Garrido

Felix Gonzalez-Martinez

Susana Losada

Adrian Plaza

Eneida Del Olmo

Jorge Mateo

Affiliations

Soon will be listed here.

Abstract

Materials And Methods: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm.

Results: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients.

Conclusions: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.

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