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Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide

Min Sue Park1, Hyeontae Jo2, Haeun Lee3

  • 1Department of Mathematics, Pohang University of Science and Technology, Pohang, Republic of Korea.

Infectious Diseases and Therapy
|February 17, 2022
PubMed
Summary

A new machine learning model accurately assesses COVID-19 severity using basic patient data. This tool aids in efficient diagnosis and guides patients to appropriate care, improving infectious disease management.

Keywords:
COVID-19Deep learningMachine learningMortalitySARS-CoV-2Triage protocol

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Public Health

Background:

  • Prompt assessment of infectious disease severity is crucial for efficient diagnosis and reducing healthcare system burden.
  • Developing a reliable severity assessment model for SARS-CoV-2 (COVID-19) can aid in patient triage and resource allocation.

Purpose of the Study:

  • To develop and validate a machine learning-based severity assessment model for COVID-19 patients.
  • To establish a system enabling patients to self-assess their COVID-19 severity and receive guidance on seeking appropriate medical attention.

Main Methods:

  • Utilized a nationwide dataset of 149,471 confirmed COVID-19 cases in Korea (February 2020 - July 2021).
  • Developed a severity assessment model using machine learning, specifically a boosting-based decision tree classifier.
  • Interpreted mortality rate as the probability score for severity assessment, requiring only basic personal data.

Main Results:

  • Achieved high model performance with a precision of approximately 0.923 and an AUROC score of 0.950.
  • Sensitivity analysis identified key variables influencing COVID-19 severity within the model.
  • The model demonstrated superior performance compared to conventional risk assessment methods.

Conclusions:

  • A high-performing, accessible SARS-CoV-2 severity assessment model was developed using nationwide data.
  • The model facilitates efficient management of infectious individuals and supports patient self-monitoring via applications.
  • The developed triage algorithm is expected to enhance patient management and healthcare system efficiency.