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Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting
N Casillas1,2, A M Torres3, M Moret4
1Departament of Internal Medicine, Hospital Virgen de la Luz, Cuenca, Spain. nazaretcasillas@hotmail.es.
Internal and Emergency Medicine
|September 13, 2022
Summary
Machine learning accurately predicts COVID-19 patient mortality by analyzing complex data patterns. This artificial intelligence approach aids in identifying high-risk individuals for improved clinical outcomes.
Area of Science:
- * Medical Informatics
- * Computational Biology
- * Public Health
Background:
- * The COVID-19 pandemic increased demand for global health assistance and research into clinical risk factors and treatments.
- * Many studies face limitations due to small sample sizes and large datasets, hindering effective analysis.
- * Machine learning (ML) offers advanced capabilities for analyzing complex data to improve clinical diagnosis and treatment strategies.
Purpose of the Study:
- * To investigate the utility of machine learning (ML) techniques in predicting mortality risk among hospitalized COVID-19 patients.
- * To evaluate the effectiveness of the eXtreme Gradient Boosting (XGBoost) algorithm in identifying patterns associated with increased clinical risk.
- * To assess treatment responses and predict patient outcomes using artificial intelligence.
Main Methods:
- * A retrospective study involving 150 hospitalized adult COVID-19 patients, divided into a Case group (deceased, n=53) and a Control group (survivors, n=98).
- * Application of a supervised learning algorithm, specifically eXtreme Gradient Boosting (XGBoost), for data analysis.
- * Evaluation of patient treatment responses and prediction of mortality risk using AI.
Main Results:
- * The XGBoost algorithm demonstrated high efficiency and portability in analyzing complex COVID-19 patient data.
- * Artificial intelligence successfully predicted patients with higher mortality rates.
- * The study achieved superior results in mortality prediction compared to other ML methods.
Conclusions:
- * Machine learning, particularly XGBoost, is a valuable tool for uncovering complex patterns in large datasets for COVID-19 research.
- * AI-driven prediction models can significantly enhance clinical diagnosis and risk stratification for COVID-19 patients.
- * This approach offers a promising avenue for improving patient management and outcomes in infectious disease outbreaks.
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