Machine-Learning-Based COVID-19 and Dyspnoea Prediction Systems for the Emergency Department

Featured Application Machine Learning-based diagnostic tool to predict SARS-CoV-2 positivity and the need of hospitalized patients for oxygen therapy when managing constrained resources in emergency departments in contingency periods. The COVID-19 pandemic highlighted an urgent need for reliable diagnostic tools to minimize viral spreading. It is mandatory to avoid cross-contamination between patients and detect COVID-19 positive individuals to cluster people by prognosis and manage the emergency department's resources. Fondazione IRCCS Policlinico San Matteo Hospital's Emergency Department (ED) of Pavia let us evaluate the exploitation of machine learning algorithms on a clinical dataset gathered from laboratory-confirmed rRT-PCR test patients, collected from 1 March to 30 June 2020. Physicians examined routine blood tests, clinical history, symptoms, arterial blood gas (ABG) analysis, and lung ultrasound quantitative examination. We developed two diagnostic tools for COVID-19 detection and oxygen therapy prediction, namely, the need for ventilation support due to lung involvement. We obtained promising classification results with F1 score levels meeting 92%, and we also engineered a user-friendly interface for healthcare providers during daily screening operations. This research proved machine learning models as a potential screening methodology during contingency times.

Automated summary

This study developed two diagnostic tools for COVID-19 detection and oxygen therapy prediction, achieving promising classification results with F1 score levels meeting 92%. The research demonstrated machine learning models as a potential screening methodology during contingency times.