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Interpretable machine learning for predicting low-dose methylprednisolone effectiveness in long COVID.

Jisheng Zhang1, Yang Chen2, Aijun Zhang2

  • 1The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Iscience
|January 26, 2026
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Summary

This study developed a predictive tool for long COVID treatment. A logistic regression model and nomogram can help personalize low-dose methylprednisolone therapy for better patient outcomes.

Keywords:
Artificial intelligence applicationsTherapeutics

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Area of Science:

  • Medical research
  • Pharmacology
  • Data science in medicine

Background:

  • Long COVID is a complex, multisystem condition with limited treatment options.
  • Individual responses to low-dose methylprednisolone vary, necessitating predictive tools.

Purpose of the Study:

  • To develop and validate a predictive model for long COVID patients receiving low-dose methylprednisolone.
  • To identify key factors influencing treatment efficacy.

Main Methods:

  • Retrospective analysis of 330 long COVID patients treated with low-dose methylprednisolone.
  • Development of machine learning models using LASSO regression.
  • Validation using training, test, and external datasets.

Main Results:

  • A logistic regression (LR) model demonstrated stable predictive performance across datasets (AUCs ranging from 0.7198 to 0.8715).
  • SHapley Additive exPlanations (SHAP) identified seven key predictive variables.
  • A nomogram was constructed based on these variables for clinical application.

Conclusions:

  • The developed LR model and nomogram are effective tools for predicting long COVID treatment response to low-dose methylprednisolone.
  • These tools support individualized treatment decisions and clinical management.
  • Further research can refine predictive accuracy for chronic disease management.