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Machine Learning for Mortality Analysis in Patients with COVID-19.
Manuel Sánchez-Montañés1, Pablo Rodríguez-Belenguer2, Antonio J Serrano-López2
1Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
International Journal of Environmental Research and Public Health
|November 17, 2020
Summary
This study identified key factors like age and oxygen saturation predicting COVID-19 patient mortality in Madrid. Machine learning models accurately forecast hospital discharge outcomes, aiding resource allocation.
Area of Science:
- Medical Informatics
- Epidemiology
- Machine Learning in Healthcare
Background:
- COVID-19 poses significant challenges to healthcare systems globally.
- Accurate prediction of patient outcomes is crucial for effective resource management.
Purpose of the Study:
- To analyze COVID-19 patient data from Madrid, Spain.
- To identify key predictors of hospital discharge outcomes (home vs. deceased).
- To develop and validate machine learning models for mortality risk prediction.
Main Methods:
- Survival analysis and logistic regression were employed.
- Supervised and unsupervised machine learning techniques, including biclustering, were utilized.
- Patient data including age, oxygen saturation, and origin (nursing home) were analyzed.
Main Results:
- Age, oxygen saturation at Emergency Rooms (ER), and nursing home origin were significant predictors of mortality.
- Developed classifiers demonstrated appreciable accuracy in predicting patient mortality.
- Biclustering identified distinct patient segments within the patient-drug dataset.
Conclusions:
- Machine learning models can effectively predict COVID-19 patient mortality.
- Interpretable decision rules derived from decision trees can guide medical care prioritization.
- Findings are vital for optimizing healthcare resource allocation during pandemics.
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