An Explainable System for Diagnosis and Prognosis of COVID-19

The outbreak of 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 paper Based on two real-world datasets 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 machine learning models It can do help to patient triaging and provide some useful advice for doctors IEEE