Machine Learning-Based Prediction of COVID-19 Prognosis Using Clinical and Hematologic Data
Fatemah O Kamel1, Rania Magadmi1, Sulafah Qutub2
1Department of Clinical Pharmacology, King Abdulaziz University Faculty of Medicine, Jeddah, SAU.
Cureus
|December 13, 2023
Summary
Machine learning accurately predicts COVID-19 patient outcomes and severity using clinical data. Hematological parameters like neutrophils and D-dimer are key predictors of disease progression and prognosis.
Area of Science:
- Medical Informatics
- Hematology
- Infectious Diseases
Background:
- The COVID-19 pandemic presents significant global healthcare challenges.
- Accurate prognosis prediction is crucial for managing COVID-19 patient care and resource allocation.
Purpose of the Study:
- To evaluate the efficacy of machine learning models in predicting COVID-19 patient outcomes and disease severity.
- To identify key clinical and hematological parameters that serve as predictors for COVID-19 prognosis.
Main Methods:
- A multicenter retrospective study involving 485 COVID-19 patients.
- Analysis of demographic data, symptoms, hematological variables, treatments, and clinical outcomes.
- Application and comparison of machine learning algorithms: random forest, multilayer perceptron, and support vector machine.
Main Results:
- Machine learning models demonstrated high performance in predicting disease severity and clinical outcomes, achieving an Area Under the Curve (AUC) of 0.96.
- Hematological parameters, specifically neutrophils, lymphocytes, D-dimer, and monocytes, were identified as the most significant predictors.
- All evaluated machine learning approaches exhibited comparable predictive capabilities.
Conclusions:
- Machine learning techniques are feasible and effective for predicting COVID-19 patient outcomes and severity.
- Hematological markers are critical indicators for assessing COVID-19 prognosis and patient outcomes.
- This study highlights the potential of leveraging routinely collected data for improved COVID-19 patient management.
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