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Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

Rosario Delgado1, Francisco Fernández-Peláez2, Natàlia Pallarés3,4

  • 1Department of Mathematics, Universitat Autònoma de Barcelona, Barcelona, Spain. Rosario.Delgado@uab.cat.

Scientific Reports
|November 18, 2024
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Summary
This summary is machine-generated.

This study introduces a novel Machine Learning approach, the Multi-Thresholding meta-algorithm, to predict COVID-19 patient risks, including Intensive Care Unit admission and mortality, effectively handling imbalanced datasets for better healthcare decisions.

Keywords:
Bayesian NetworksCOVID-19 patient risks assessmentCost-sensitive Machine Learning modellingHealthcare decision-makingMulticlass classification thresholding

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Area of Science:

  • Machine Learning
  • Medical Informatics
  • Computational Biology

Background:

  • COVID-19 patient outcomes vary significantly, with a subset facing severe complications like Intensive Care Unit (ICU) admission or mortality.
  • Predicting these severe outcomes is challenging due to imbalanced datasets, where severe cases are minority classes.
  • Existing models often struggle with bias and accuracy in multiclass classification tasks with imbalanced data.

Purpose of the Study:

  • To develop and validate a Machine Learning model for predicting critical COVID-19 patient outcomes (ICU admission, mortality).
  • To address the challenge of imbalanced datasets in multiclass classification for patient risk assessment.
  • To identify key risk and protective factors influencing severe COVID-19 outcomes.

Main Methods:

  • Development of the Multi-Thresholding meta-algorithm (MTh) for multiclass imbalanced classification.
  • Integration of Bayesian networks with the MTh algorithm for a robust predictive model.
  • Utilizing patient admission data to train and evaluate the predictive model.

Main Results:

  • The MTh algorithm effectively manages dataset imbalance, improving predictive accuracy for minority classes.
  • Identified significant risk factors including high Charlson Index and pre-existing conditions for ICU admission and mortality.
  • Developed an explanatory model revealing interrelationships between factors and therapeutic limits.

Conclusions:

  • The novel Machine Learning approach offers a significant advancement in predicting COVID-19 patient risks from imbalanced data.
  • The model enhances decision-making in healthcare, potentially improving patient outcomes and resource allocation.
  • This research provides a valuable tool for clinical risk assessment and management of infectious diseases.