TY - JOUR
T1 - Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling
AU - Mahboub, Bassam
AU - Bataineh, Mohammad T.Al
AU - Alshraideh, Hussam
AU - Hamoudi, Rifat
AU - Salameh, Laila
AU - Shamayleh, Abdulrahim
N1 - Funding Information:
The authors would like to thank Dubai Scientific Research Ethical Committee (DSREC), Dubai Health Authority and Rashid hospital for their support in this study. Funding. RH was funded by University of Sharjah COVID19 grant (Grant no: CoV19-0308), Sharjah Research Academy (Grant no: MED001), and University of Sharjah (Grant no: 1901090254).
Funding Information:
RH was funded by University of Sharjah COVID19 grant (Grant no: CoV19-0308), Sharjah Research Academy (Grant no: MED001), and University of Sharjah (Grant no: 1901090254).
Publisher Copyright:
© Copyright © 2021 Mahboub, Bataineh, Alshraideh, Hamoudi, Salameh and Shamayleh.
PY - 2021/5/4
Y1 - 2021/5/4
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - COVID-19
KW - length of stay
KW - predictive analytics
KW - risk of death
UR - http://www.scopus.com/inward/record.url?scp=85105983776&partnerID=8YFLogxK
U2 - 10.3389/fmed.2021.592336
DO - 10.3389/fmed.2021.592336
M3 - Article
AN - SCOPUS:85105983776
SN - 2296-858X
VL - 8
JO - Frontiers in Medicine
JF - Frontiers in Medicine
M1 - 592336
ER -