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Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS".

Alessio Mancini1,2, Leonardo Vito3,4, Elisa Marcelli5

  • 1School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy. alessio.mancini@unicam.it.

BMC Bioinformatics
|August 26, 2020
PubMed
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This summary is machine-generated.

This study developed a machine learning model to predict multidrug-resistant urinary tract infections (MDR UTIs) in hospitalized patients. The Catboost model demonstrated superior accuracy, offering a valuable tool for early detection and management of MDR UTIs.

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Infectious Disease Prediction

Background:

  • Hospital-acquired infections, particularly multidrug-resistant urinary tract infections (MDR UTIs), pose a significant clinical challenge.
  • Effective prediction models are crucial for antibiotic stewardship and patient management.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting the risk of MDR UTIs in hospitalized patients.
  • To integrate a user-friendly cloud platform (DSaaS) for healthcare professionals to analyze patient data and validate predictive models.

Main Methods:

  • Utilized a real-world antibiotic stewardship dataset from 1486 hospitalized patients with nosocomial UTIs.
  • Employed machine learning algorithms including Catboost, Support Vector Machine, and Neural Networks.
Keywords:
Antibiotic stewardshipClassificationData science pipelineMachine learningMulti drug resistanceNosocomial infectionRegression

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  • Leveraged the Data Science as a Service (DSaaS) platform for model development and performance evaluation using metrics like accuracy, AUC-ROC, AUC-PRC, F1 score, sensitivity, specificity, and MCC.

    • Main Results:

    • The Catboost model achieved the highest predictive performance across all evaluated metrics (MCC 0.909, Sensitivity 0.904, F1 score 0.809, AUC-PRC 0.853, AUC-ROC 0.739, Accuracy 0.717).
    • Predictive accuracy was based on five easily accessible patient hospitalization predictors: sex, age, age class, ward, and time period.

    Conclusions:

    • The DSaaS platform, powered by the Catboost model, can serve as a valuable decision support tool for physicians in managing patients at high risk for MDR UTIs.
    • Future enhancements for DSaaS include unsupervised learning, streaming data analysis, and big data capabilities for comprehensive data analysis pipelines.