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March 8, 2026PLoS ONE0 citationsOpen Access

Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients

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JMJúlia Chaves Neuenschwander MagalhãesAFAlexandre Dias Porto Chiavegatto Filho

Key Points

  • The aim is to examine how machine learning models predict mortality risk in COVID-19 patients across different subgroups.
  • Conducted a multicohort analysis of COVID-19 patients
  • Evaluated various machine learning algorithms
  • Compared individual and subgroup prediction outcomes
  • Models showed similar overall performance but diverged significantly in individual predictions
  • No single algorithm consistently outperformed others across all patient subgroups
  • Findings emphasize the limitations of global performance metrics

Abstract

This study demonstrates that ML models with similar overall performance can yield substantially divergent predictions at both the individual and subgroup levels, and that no single algorithm consistently outperforms others across all patient subgroups. These findings highlight the limitations of relying solely on global performance metrics and underscore the need for context-aware evaluation of ML models in heterogeneous clinical populations.

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Cite This Study

Magalhães et al. (2026) studied this question.

synapsesocial.com/papers/69ada8dfbc08abd80d5bc4abhttps://doi.org/10.1371/journal.pone.0344354
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting mortality outcomes in individual COVID-19 patients using machine learning algorithms2024 · 1 citations
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  4. 4Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction2024 · 2 citations
  5. 5Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data2025