Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research

Giacobbe, Daniele Roberto; Mora, Sara; Giacomini, Mauro; Bassetti, Matteo

doi:10.3390/antibiotics9020054

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Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research

Daniele Roberto Giacobbe

^1,2,*

Sara Mora

³,

Mauro Giacomini

and

Matteo Bassetti

^1,2

Department of Health Sciences (DISSAL), University of Genoa,16132 Genoa, Italy

Clinica Malattie Infettive, Ospedale Policlinico San Martino—IRCCS, 16132 Genoa, Italy

Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, 16145 Genoa, Italy

Author to whom correspondence should be addressed.

Antibiotics 2020, 9(2), 54; https://doi.org/10.3390/antibiotics9020054

Received: 20 December 2019 / Revised: 22 January 2020 / Accepted: 29 January 2020 / Published: 31 January 2020

(This article belongs to the Special Issue Stewardship of Antibiotics for Multidrug-Resistant Gram-Negative Bacteria)

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Abstract

The dissemination of multidrug-resistant Gram-negative bacteria (MDR-GNB) is associated with increased morbidity and mortality in several countries. Machine learning (ML) is a branch of artificial intelligence that consists of conferring on computers the ability to learn from data. In this narrative review, we discuss three existing examples of the application of ML algorithms for assessing three different types of risk: (i) the risk of developing a MDR-GNB infection, (ii) the risk of MDR-GNB etiology in patients with an already clinically evident infection, and (iii) the risk of anticipating the emergence of MDR in GNB through the misuse of antibiotics. In the next few years, we expect to witness an increasingly large number of research studies perfecting the application of ML techniques in the field of MDR-GNB infections. Very importantly, this cannot be separated from the availability of a continuously refined and updated ethical framework allowing an appropriate use of the large datasets of medical data needed to build efficient ML-based support systems that could be shared through appropriate standard infrastructures. View Full-Text

Keywords: antimicrobial resistance; Gram-negative; MDR; machine learning

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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MDPI and ACS Style

Giacobbe, D.R.; Mora, S.; Giacomini, M.; Bassetti, M. Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research. Antibiotics 2020, 9, 54. https://doi.org/10.3390/antibiotics9020054

AMA Style

Giacobbe DR, Mora S, Giacomini M, Bassetti M. Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research. Antibiotics. 2020; 9(2):54. https://doi.org/10.3390/antibiotics9020054

Chicago/Turabian Style

Giacobbe, Daniele R., Sara Mora, Mauro Giacomini, and Matteo Bassetti. 2020. "Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research" Antibiotics 9, no. 2: 54. https://doi.org/10.3390/antibiotics9020054

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Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research

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