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Related Experiment Video

Updated: Jan 31, 2026

A Precise Pathogen Delivery and Recovery System for Murine Models of Secondary Bacterial Pneumonia
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PreBP: an interpretable, optimized ensemble framework using routine complete blood count for rapid pathogen

Xiaoxi Hao1, Dingjian Liang2, Yimin Shen1

  • 1School of Computer, Electronics and Information, Guangxi University, Nanning, China.

Frontiers in Bioinformatics
|January 30, 2026
PubMed
Summary

Rapid bacterial pneumonia pathogen identification is now possible using interpretable ensemble learning (PreBP) with complete blood count (CBC) data. This computational approach aids early clinical decisions, complementing traditional culture methods.

Keywords:
SHapley additive explanationsbacterial pneumoniacomplete blood countensemble learninginterpretable machine learningpathogen identification

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Area of Science:

  • Computational biology and machine learning applications in infectious disease diagnostics.

Background:

  • Bacterial pneumonia diagnosis requires timely pathogen identification for effective treatment.
  • Conventional methods like sputum or blood cultures are time-consuming and labor-intensive.

Purpose of the Study:

  • To develop an interpretable ensemble learning framework (PreBP) for rapid bacterial pneumonia pathogen identification.
  • To utilize routinely available complete blood count (CBC) parameters for pathogen detection.

Main Methods:

  • Analyzed 1,334 CBC samples from patients with culture-confirmed bacterial pneumonia.
  • Employed five machine learning models including XGBoost, MLPNN, AdaBoost, RF, and ExtraTrees.
  • Utilized metaheuristic-optimized hyperparameters, dual-phase feature selection (Lasso and Boruta), and SHapley additive explanations (SHAP) for interpretability.

Main Results:

  • The PreBP framework achieved an AUC of 0.920.
  • PreBP demonstrated high performance with 87.1% precision and 86.7% accuracy and sensitivity.

Conclusions:

  • PreBP offers an interpretable and computational approach for pathogen identification in bacterial pneumonia using routine CBC data.
  • The framework can provide supplementary evidence for earlier clinical decision-making, supporting culture-dependent workflows.