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Predicting central line-associated bloodstream infections and mortality using supervised machine learning.

Joshua P Parreco1, Antonio E Hidalgo1, Alejandro D Badilla2

  • 1Department of Surgery, University of Miami Miller School of Medicine, USA.

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|February 28, 2018
PubMed
Summary

Machine learning models can predict central line-associated bloodstream infections (CLABSI). Deep learning showed promise for mortality and central line placement, while logistic regression predicted CLABSI effectively.

Keywords:
Artificial intelligenceCentral line-associated bloodstream infectionHospital-acquired infectionsMachine learningQuality improvementSeverity of illness score

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Area of Science:

  • Healthcare Informatics
  • Machine Learning in Medicine
  • Infectious Disease Epidemiology

Background:

  • Central line-associated bloodstream infections (CLABSI) are a significant healthcare concern.
  • Predictive modeling can aid in early identification and prevention of CLABSI.
  • Machine learning offers advanced techniques for analyzing complex patient data.

Purpose of the Study:

  • To compare the efficacy of different machine learning techniques in predicting CLABSI.
  • To evaluate the performance of logistic regression, gradient boosted trees, and deep learning models.
  • To identify optimal algorithms for predicting patient outcomes including CLABSI.

Main Methods:

  • Utilized the Multiparameter Intelligent Monitoring in Intensive Care III database.
  • Included ICU admissions with severity of illness scores, components, comorbidities, and outcomes.
  • Developed predictive models using logistic regression, gradient boosted trees, and deep learning classifiers.

Main Results:

  • Deep learning models achieved the highest AUC for mortality (0.885±0.010) and central line placement (0.816±0.006).
  • Logistic regression demonstrated an AUC of 0.722±0.048 for predicting CLABSI.
  • The study analyzed 57,786 hospital admissions with a 1.5% CLABSI rate.

Conclusions:

  • Developed models for early identification of patients at risk for CLABSI.
  • Early CLABSI detection can lead to improvements in healthcare quality, cost-efficiency, and patient outcomes.
  • Machine learning provides valuable tools for proactive management of healthcare-associated infections.