# Machine-Learning-Based COVID-19 and Dyspnoea Prediction Systems for the Emergency Department
###### Abstract  
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 Polnicnico 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. blood tests; diagnosis; dyspnoea; lung ultrasound; machine learning; SARS-CoV-2 +
Footnote †: journal: Medical  
The need for quick and reliable tools to diagnose and stratify patients' prognoses to optimize resource allocation is of the utmost importance. Splitting the emergency department (ED) into clean and dirty areas during a pandemic is fundamental to preventing patient-to-patient spreading. During the last decade, the number of research articles on artificial intelligence (AI) as a resource for all kinds of medical specialties highly increased, proving machine learning (ML) algorithms to be successful [7]. Notably, several studies report medical instruments supported by AI concerning applications ranging from stroke management to cancer detection [8; 9; 10; 11; 12; 13]. AI is reshaping how biomarkers and biosignals are defined and managed [8; 9]. AI-enabled support systems might aid clinicians in decision making, especially during ED triage. Indeed, most studies aim to develop models to schedule patients according to their triage acuity level, assessing the severity of their conditions and deciding upon hospital admission [14]. SARS-CoV-2 studies comprise algorithms exploiting computed tomography (CT), lung ultrasound (LUS), and X-ray imaging techniques to diagnose and examine COVID-19 evolving patterns [2; 15; 16; 17; 18]. On the other hand, researchers exploited ML- and AI-based techniques to tackle contact tracing, the prediction and forecasting of epidemiological measurements, and SARS-CoV-2 drug development [19; 20; 21]. During the first heavy pandemic waves, several governments limited swab testing due to the unfeasible number of them to be carried out. Therefore, from that period onward, research has focused on machine learning algorithms to quickly assess patients for COVID-19 positivity and mortality [20; 21; 22; 23; 24; 25]. The studies confirmed the feasibility of the statistical learning-based approach. Nonetheless, only 8% of the studies observed by literature reviews focused on blood test analyses [21]. Researchers produced statistical models having different goals ranging from COVID-19 to mortality prediction, with classification results having sensitivity levels of approximately 80-89% [21; 26]. 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. The main goal was to quickly stratify patients and employ cross-contamination procedures, avoiding extensive swab testing and leveraging physicians' workload. We gathered patients' data based on two primary principles. First, we engaged features readily available in every ED triage, such as anamnesis, symptoms, and vital signs.