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

Journal: BMC bioinformatics
PMID: 32838752

Abstract

BACKGROUND: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms.

Authors

  • Alessio Mancini
    School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy. alessio.mancini@unicam.it.
  • Leonardo Vito
    School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.
  • Elisa Marcelli
    School of Science and Technology, Mathematics Division, University of Camerino, Camerino, Italy.
  • Marco Piangerelli
    Computer Science Division, School of Science and Technology, University of Camerino, Camerino, Italy.
  • Renato De Leone
    School of Science and Technology, Mathematics Division, University of Camerino, Camerino, Italy.
  • Sandra Pucciarelli
    School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.
  • Emanuela Merelli
    School of Science and Technology, University of Camerino, Via del Bastione, Camerino, Italy.

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