JMIR Publications

ABSTRACT

Background:

COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments.

Objective:

To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet, to predict in-hospital mortality using a routine blood sample at the time of hospital admission.

Methods:

We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining a deep neural network and random forest model. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions.

Results:

In the testing datasets, EDRnet provided high sensitivity (100%), specificity (91.35%), and accuracy (91.51%). To extend the number of patient data, we developed a web application (http://beatcovid19.ml/), where anyone can access the model to predict the mortality and can register his or her own blood laboratory results.

Conclusions:

Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help healthcare providers fight COVID1-19 and improve patients’ outcome.