Approaches for Enhancing Pre-hospital EMS Response during the COVID-19 Pandemic Machine Learning

Abstract

Background: Coronavirus disease 2019 (COVID-19) caused an unprecedented healthcare crisis and warranted a need to use artificial intelligence (AI) and machine learning (ML) for enhancing caller screening and triage within pre-hospital Emergency Medical Services (EMS) specifically tailored to COVID-19 cases. This study aimed to analyze existing AI and ML models and assess their accuracy and precision. Methods: A comprehensive assessment of artificial intelligence (AI) applications used to improve EMS responses in the context of COVID-19 instances was done. The dataset produced by Mexican government was used. This dataset was assessed over different models encompassing logistic regression, random forest, gradient boosting, neural networks, k-nearest neighbors (KNN), Naive Bayes, and clustering (K-means). Results: Multiple models performance evaluation was done employing metrics such as accuracy, precision, recall, and F1-score to comprehensively assess the strengths and limitations of these models. Conclusion: The study\'s findings underline the complexities inherent in caller screening and triage for COVID-19 cases, showcasing diverse strengths and limitations within the deployed machine learning models. The discourse underscores the necessity for a multifaceted approach to effectively manage the intricate challenges associated with caller classification and triage, offering invaluable insights for future research endeavors and guiding the enhancement of emergency healthcare systems.