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Related Experiment Videos

Comparing risk factors in severe COVID-19 using machine learning and non-machine learning methods: analysis from 2

Christian Møller Jensen1, Ramtin Zargari Marandi1, Kasper Sommerlund Moestrup1

  • 1CHIP, Centre of Excellence for Health, Immunity, and Infections, Rigshospitalet, University of Copenhagen, Copenhagen Ø, 2100, Denmark.

JAMIA Open
|June 25, 2026
PubMed
Summary

Machine learning (ML) models identified COVID-19 risk factors and predicted mortality, showing promise when combined with traditional methods. External validation in randomized controlled trials is crucial for reliable risk factor identification.

Keywords:
SARS-CoV-2biostatisticsmachine learningmedical informaticsrandomized controlled trial

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Epidemiology
  • Clinical Trials

Background:

  • Accurate prediction of COVID-19 mortality is essential for patient management.
  • Machine learning (ML) offers advanced analytical capabilities for identifying risk factors and predicting outcomes.
  • Comparing ML models with traditional statistical methods is vital for understanding their respective strengths.

Purpose of the Study:

  • To compare risk factor identification and 90-day mortality prediction between two ML models and a non-ML model.
  • To investigate the validity of ML models in an external cohort of hospitalized COVID-19 patients.
  • To assess the performance of ML models in predicting clinical outcomes.

Main Methods:

  • Utilized prospectively collected data from two randomized controlled trials (RCTs): TICO/ACTIV-3 (derivation/internal validation) and ITAC (external validation).
  • Employed an ML pipeline with classification and survival models for risk factor identification and outcome prediction.
  • Compared risk factors identified by ML models against a traditional Cox model and evaluated model performance across cohorts.

Main Results:

  • A total of 3204 hospitalized COVID-19 patients were included (2625 in TICO/ACTIV-3, 579 in ITAC).
  • Identified overlapping and unique risk factors across ML and non-ML models, with five factors common to all.
  • The ML model demonstrated good predictive performance in the derivation cohort and remained above chance level in the external validation cohort, despite a performance decrease.

Conclusions:

  • Integrating ML and non-ML methods may enhance risk factor identification for COVID-19.
  • External validation of ML models, even within RCTs, is critical for ensuring generalizability and reliability.
  • The study highlights the potential of ML in clinical outcome prediction while emphasizing the need for rigorous validation.