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June 1, 2026npj Digital MedicineOpen Access

Development and validation of a machine learning-based diagnostic system for 22 pediatric respiratory pathogens: a large-scale multicenter study

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Authors

DSDubin SuQCQun ChenRXRuizhi Xu

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Overview

Randomized trial demonstrates improved pathogen identification in pediatric respiratory infections, suggesting enhanced treatment strategies.

Key Points

  • This study aims to develop and validate a machine learning-based diagnostic system for identifying pediatric respiratory pathogens.
  • Multicenter study involved 134,500 hospitalized children across three clinical centers.
  • Integrated 42 clinical and laboratory features from electronic health records for accurate pathogen identification.
  • Prospective validation conducted on an independent cohort of 1338 children.
  • Pathog-PDx distinguished 22 pathogen subtypes and outperformed conventional models.
  • Achieved high classification performance for influenza virus (AUC = 0.95; Sn: 0.88; Sp: 0.86).
  • Mean AUCs of 0.88 for various pathogens of respiratory tract infections.

Cite This Study

Su et al. (2026) studied this question.

synapsesocial.com/papers/6a1d21e502fbce9130637c42https://doi.org/10.1038/s41746-026-02818-9
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