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Related Experiment Video

Updated: May 20, 2025

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Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

Adhemar Villani Júnior1, Maristela P Freire2, Felippe Lazar Neto3

  • 1School Of Arts, Sciences and Humanities, University of Sao Paulo, Sao Paulo, Brazil.

European Journal of Cancer (Oxford, England : 1990)
|May 18, 2025
PubMed
Summary

Machine learning accurately predicts bloodstream infections in chemotherapy patients, even non-neutropenic cases. This tool can guide clinical treatment and infection diagnosis.

Keywords:
EnterobacteralesLeukemiaMonocytopeniaPredictive modelingSolid tumor

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Area of Science:

  • Oncology
  • Infectious Diseases
  • Medical Informatics

Background:

  • Bloodstream infections (BSIs) are a significant concern for patients undergoing chemotherapy.
  • Early and accurate prediction of BSIs can improve patient outcomes and guide timely interventions.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) model for predicting bloodstream infections (BSIs) in cancer patients receiving chemotherapy.
  • To identify key predictors of BSI in this vulnerable patient population.

Main Methods:

  • A retrospective analysis of 107,757 chemotherapy cycles from 19,225 cancer patients (2017-2022).
  • Data included patient demographics, cancer type, chemotherapy details, and laboratory results.
  • Multiple ML algorithms were tested, with feature importance assessed using SHapley Additive exPlanations (SHAP).

Main Results:

  • The best performing model was a neural network, achieving 91.93% AUC, 70.7% sensitivity, and 93.49% specificity.
  • Key predictors for BSI included the first chemotherapy cycle, antimetabolite use, palliative chemotherapy, monocytopenia, and hematological malignancies.
  • BSI occurred in 1.33% of cycles, with a notable proportion in non-neutropenic patients.

Conclusions:

  • Machine learning models can effectively predict bloodstream infections in chemotherapy patients.
  • The developed ML model shows potential for clinical application to guide treatment and infection workup, including in non-neutropenic cases.