Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling

Frontiers in Medicine

article

Bassam Mahboub¹  Mohammad T. Al Bataineh²  Hussam Alshraideh³  Rifat Hamoudi¹  Laila Salameh²  Abdulrahim Shamayleh³ 

[1] Clinical Sciences Department, College of Medicine, University of Sharjah, United Arab Emirates;Sharjah Institute for Medical Research, University of Sharjah, United Arab Emirates;Industrial Engineering Department, American University of Sharjah, United Arab Emirates;Industrial Engineering Department, Jordan University of Science and Technology;Division of Surgery and Interventional Science, University College London, United Kingdom

关键词: artificial intelligence;    COVID-19;    length of stay;    predictive analytics;    risk of death;   

DOI  :  10.3389/fmed.2021.592336

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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 R 2 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.

【 授权许可】

CC BY