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

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.

[1]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[2]  Jing Zhao,et al.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia , 2020, The New England journal of medicine.

[3]  Mohammad Pourhomayoun,et al.  Interactive Dimensionality Reduction for Improving Patient Adherence in Remote Health Monitoring , 2018, 2018 International Conference on Computational Science and Computational Intelligence (CSCI).

[4]  M. Kraemer,et al.  Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel , 2020, Journal of travel medicine.

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  Mohammad Pourhomayoun,et al.  Risk Prediction of Critical Vital Signs for ICU Patients Using Recurrent Neural Network , 2019, 2019 International Conference on Computational Science and Computational Intelligence (CSCI).

[7]  John S. Brownstein,et al.  Epidemiological data from the COVID-19 outbreak, real-time case information , 2020, Scientific Data.

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  Hassan Ghasemzadeh,et al.  Multiple model analytics for adverse event prediction in remote health monitoring systems , 2014, 2014 IEEE Healthcare Innovation Conference (HIC).

[10]  Mohammad Pourhomayoun,et al.  Interactive Predictive Analytics for Enhancing Patient Adherence in Remote Health Monitoring , 2018 .

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  Melanie Kwon,et al.  Multi-label Classification of Single and Clustered Cervical Cells Using Deep Convolutional Networks , 2018 .