Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling - Mahboub, Bassam; Bataineh, Mohammad T. Al; Alshraideh, Hussam; Hamoudi, Rifat; Salameh, Laila; Shamayleh, Abdulrahim (Frontiers in Medicine, 2021)
Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in Dubai health authority and developed predictive models to predict the patient’s length of hospital stay and risk of death. A Decision Tree (DT) based model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed a very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5% and 87.8%, respectively and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns and Receiver Operator Characteristic (ROC) approach suggested a stable and robust DT model performance. The results showed that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used Anti-coagulant medications. Fortunately, intubated patients who are using Anti-coagulant and dexamethasone medications with International Normalized Ratio of less than 1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence-based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front-line to enhance management strategies to save lives.