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 <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.
Highlights
The current century has witnessed several emerging pandemics, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), Ebola, and Zika viruses
SARS-CoV-2 infection was confirmed by real-time reversetranscription polymerase-chain-reaction (RT-PCR) assay from nasal and pharyngeal swab specimens according to their ICD10 disease code for all patients enrolled in this study
Comparisons of obtained models show that the selected attributes and their cut points were very similar across all model variations, indicating a robust decision tree (DT) model
Summary
The current century has witnessed several emerging pandemics, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), Ebola, and Zika viruses. Since December 2019, the world has been impacted by the spread of the coronavirus SARS-CoV2. The coronavirus disease 2019 is an infectious disease that causes severe acute respiratory illness. March 2020, the World Health Organization (WHO) characterized the COVID-19 outbreak as a pandemic due to the alarming levels of spread and severity. The fast spread of COVID-19 posed a significant challenge to healthcare systems and to hospitals due to the surge in caseload per hospital [1]. There is a vital need to precisely predict and triage cases and rank those with high probability to progress to a higher level of illness severity
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