Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

Abstract

In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

“…Sputum, supraphysiologic RR, and decreased SpO 2 were directly related to pulmonary abnormalities in COVID-19. Elevated BUN, increased D-dimer, and lymphocytopenia might indicate extrapulmonary disorders and were potentially correlated with multiorgan damage caused by COVID-19 Available ML-based studies on prognosis prediction of COVID-19 patients are impeded by limited sample size, category of variables for prediction, short-term follow-ups for outcomes, and paucity of independent external validation [19][20][21][22][23][24][25][26] . To overcome these obstacles, we included 2520 consecutive inpatients with definite outcomes and detailed baseline characteristics within a specific time period for training and multiple validations of MRPMC to avoid overfitting and ensure general applicability, reproducibility, and credibility.…”

Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal

“…We believe that the combination of quantitative analysis data, lung volumes, and structured report items, with the help of deep learning techniques, will enable digital patient models to be extracted; therefore, the progression of the disease and the possible response to pharmacological treatments could be studied from these predictive and prognostic models. For this reason, many research groups have launched single and multicenter studies for the application of artificial intelligence on CT data [6,33,[51][52][53][54][55].…”

Can AI help in the fight against COVID ‐19?

“…34 In another study, neural networks trained on 42 clinical and demographic factors demonstrated 94% accuracy in predicting mortality. 35 • In identifying patients at risk of long term hospitalisation, a machine learning model trained on CT imaging data was able to identify such patients with predictive accuracy of 95%. 36 Perspectives MJA 213 (10) ▪ 16 November 2020 natural language processing applied to the PubMed database have identified a poly-ADP-ribose polymerase 1 inhibitor (CVL218) as a potential candidate, currently undergoing clinical testing.…”

“…Some ML algorithms can more accurately estimate risk of death, development of acute respiratory distress syndrome, and duration of hospitalisation (Box 3). [32][33][34][35][36]…”