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Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.

Daniel I Morís1,2, Joaquim de Moura1,2, Pedro J Marcos3

  • 1Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071 A Coruña, Spain.

Biomedical Signal Processing and Control
|March 14, 2023
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Summary

Machine learning models can predict COVID-19 patient outcomes, identifying high-risk individuals needing hospitalization or facing mortality. This aids healthcare systems in optimizing resource allocation for critical cases.

Keywords:
COVID-19ClassificationClinical dataFeature selectionMachine learning

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Public Health

Background:

  • The COVID-19 pandemic poses a significant global health challenge, straining healthcare systems.
  • Clinical decision-making during the pandemic is complex, often requiring resource management amidst scarcity.
  • Computer-aided diagnosis systems offer potential for clinical support and disease analysis.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting COVID-19 patient outcomes.
  • To identify clinical factors associated with high risk of death and hospitalization in COVID-19 patients.
  • To support healthcare resource management by identifying patients requiring intensive medical care.

Main Methods:

  • Utilized several machine learning algorithms to analyze clinical data from COVID-19 patients.
  • Conducted two primary studies: predicting risk of death and predicting need for hospitalization.
  • Evaluated model performance using metrics such as AUC-ROC.

Main Results:

  • Identified key clinical features relevant to patient outcomes in both risk of death and hospitalization prediction.
  • The XGBoost algorithm demonstrated strong performance in predicting hospitalization (AUC-ROC ) and risk of death (AUC-ROC ).
  • Results highlight the potential for machine learning to accurately stratify patient risk.

Conclusions:

  • Machine learning models show significant promise in predicting COVID-19 patient outcomes.
  • Accurate prediction of patient risk can enhance clinical decision-making and optimize healthcare resource allocation.
  • This approach provides a valuable tool for healthcare services to manage resources effectively during health crises.