1. Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling
- Author
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Bassam Mahboub, Mohammad T. Al Bataineh, Hussam Alshraideh, Rifat Hamoudi, Laila Salameh, and Abdulrahim Shamayleh
- Subjects
Medicine (General) ,Coronavirus disease 2019 (COVID-19) ,Receiver operating characteristic ,business.industry ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Separation (statistics) ,Decision tree ,COVID-19 ,risk of death ,General Medicine ,Predictive analytics ,artificial intelligence ,predictive analytics ,R5-920 ,length of stay ,Medicine ,Median absolute deviation ,Risk of death ,Artificial intelligence ,business ,Original Research - Abstract
Severe acute respiratory syndrome coronavirus 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 the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed 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 a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of
- Published
- 2021