An Explainable System for Diagnosis and Prognosis of COVID-19

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.

Automated summary

The outbreak of Coronavirus Disease-2019 (COVID-19) poses a threat to global health, especially putting pressure on healthcare systems in developing countries. In order to address the diagnosis and prognosis of COVID-19, a data-driven medical assistance system is proposed, which uses machine learning to predict infection probability and mortality of patients.