Artificial Intelligence Framework for COVID19 Patients Monitoring

• Published in: 2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME) pp. 213 - 218
• Main Authors: Rihana, Sandy, Bou Rjeily, Christelle
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.10.2021
• Subjects: Artificial Neural Networks; Biological system modeling; Boosting; COVID-19; COVID19; Decision Tree; Gaussian Naïve Bayes; Gradient Boosting; Hospitals; ICU; machine learning; Mathematical models; prediction models; Predictive models; Random Forest; Support vector machines
• ISSN: 2377-5696
• DOI: 10.1109/ICABME53305.2021.9604816

The current global spread of COVID-19, a highly contagious disease, has challenged healthcare systems and placed immense burdens on medical staff globally. Almost 5% to 10% among hospitalized patients will require ICU admission. Predicting ICU admission can help in managing better the patient and the healthcare system. This study aims to develop a model that can predict whether a COVID-19 patient, who has already been admitted to the hospital, will enter the ICU or not. This could be accomplished by monitoring his vital signs, and blood tests, and inquiring about his demographic records, during his stay in the hospital. Multiple models, including Artificial Neural Networks, Logistic Regression, Decision Tree, Random Forest, Gaussian Naïve Bayes, Gradient Boosting, and Support Vector Machines, were designed and implemented using MATLAB and Python. Random Forest, Decision Tree, and Gradient Boosting, are examples of decision tree-based algorithms that outperformed the others. The Random Forest (Accuracy: 99.12%, Cross-Validation Accuracy 86.34%) and Decision Tree (Accuracy: 99.12%, Cross-Validation Accuracy 79.48%) and Gradient Boosting (Accuracy: 93.77%, Cross-Validation Accuracy: 86.96%) had the highest accuracy scores as compared to other models such as the Support Vector Machines (Accuracy: 87.74%, Cross-Validation Accuracy 72.42%). In future work, the aim will be to predict whether a patient will join ICU or not, based on monitoring for multiple windows. As a result, high accuracy scores will be reached, since the model will analyze the vital signs and laboratory data at multiple stages and timings. In this way, anticipating the requirement for ICU admission well ahead of time.