ABSTRACT
The outbreak of Coronavirus Disease-2019 (COVID-19) has posed a threat to world health. With the increasing number of people infected, healthcare systems, especially those in developing countries, are bearing tremendous pressure. There is an urgent need for the diagnosis of COVID-19 and the prognosis of inpatients. To alleviate these problems, a data-driven medical assistance system is put forward in this article. Based on two real-world data sets in Wuhan, China, the proposed system integrates data from different sources with tools of machine learning (ML) to predict COVID-19 infected probability of suspected patients in their first visit, and then predict mortality of confirmed cases. Rather than choosing an interpretable algorithm, this system separates the explanations from ML models. It can do help to patient triaging and provide some useful advice for doctors.
ABSTRACT
The Covid-19 pandemic represents one of the greatest global health emergencies of the last few decades with indelible consequences for all societies throughout the world. The cost in terms of human lives lost is devastating on account of the high contagiousness and mortality rate of the virus. Millions of people have been infected, frequently requiring continuous assistance and monitoring. Smart healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of patient care but also in order to support the early diagnosis, prevention and evaluation of Covid-19 in a faster and more accurate way. On the other hand, the necessity to realise reliable and precise smart healthcare solutions, able to acquire and process voice signals by means of appropriate Internet of Things devices in real-time, requires the identification of algorithms able to discriminate accurately between pathological and healthy subjects. In this paper, we explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis. Several studies report, in fact, significant effects of this virus on voice production due to the considerable impairment of the respiratory apparatus. Vocal folds oscillations that are more asynchronous, asymmetrical and restricted are observed during phonation in Covid-19 patients. Voice sounds selected by the Coswara database, an available crowd-sourced database, have been e analysed and processed to evaluate the capacity of the main ML techniques to distinguish between healthy and pathological voices. All the analyses have been evaluated in terms of accuracy, sensitivity, specificity, F1-score and Receiver Operating Characteristic area. These show the reliability of the Support Vector Machine algorithm to detect the Covid-19 infections, achieving an accuracy equal to about 97%.