The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus

Joana Rosado Coelho, João André Carriço, Daniel R. Knight, José Luis Martínez, Ian Morrissey, Marco Rinaldo Oggioni, Ana Teresa Freitas

PLoS ONE 2013 article cited by 52

Source Open-access PDF More in Medicine & Health

importance 6/10 ai assisted Antimicrobial Resistance in Staphylococcus infectious disease stage: research

Problem

Whether reduced susceptibility to biocides co-selects for antibiotic resistance in natural clinical isolates of Staphylococcus aureus was unclear.

Why it matters

Antibiotic resistance threatens treatment of infectious diseases, and biocide use might worsen it.

What the AI did

Machine learning methodologies performed an unbiased, blind-based evaluation of antibiotic and biocide susceptibility across 1632 worldwide clinical isolates.

Results

not stated in source

Impact

The evidence only supports that ML was applied to a large isolate dataset; no outcome is stated.

Record

Venue	PLoS ONE
Published	2013
Work type	article
Authors	Joana Rosado Coelho, João André Carriço, Daniel R. Knight, José Luis Martínez, Ian Morrissey, Marco Rinaldo Oggioni, Ana Teresa Freitas
Citations	52 (OpenAlex, at import time)
References	60
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1371/journal.pone.0055582
Open access	gold
Keywords	machine learning, Staphylococcus aureus, antibiotic resistance, biocides, susceptibility

Importance rating

6 / 10

significant — thousands to millions affected

Abstract states rising antibiotic resistance is a significant problem for treatment of infectious diseases; specific question of biocide co-selection is a narrower mechanistic issue within AMR.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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