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Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit

1
Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid 28943, Spain
2
Intensive Care Department, University Hospital of Fuenlabrada, Madrid 28942, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(6), 603; https://doi.org/10.3390/e21060603
Received: 8 April 2019 / Revised: 4 June 2019 / Accepted: 13 June 2019 / Published: 18 June 2019
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Abstract

The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for the patient, for the system, and for society in general. Because of the critical health status of patients in the intensive care unit (ICU), time is critical to identify bacteria and their resistance to antibiotics. Since common antibiotics resistance tests require between 24 and 48 h after the culture is collected, we propose to apply machine learning (ML) techniques to determine whether a bacterium will be resistant to different families of antimicrobials. For this purpose, clinical and demographic features from the patient, as well as data from cultures and antibiograms are considered. From a population point of view, we also show graphically the relationship between different bacteria and families of antimicrobials by performing correspondence analysis. Results of the ML techniques evidence non-linear relationships helping to identify antimicrobial resistance at the ICU, with performance dependent on the family of antimicrobials. A change in the trend of antimicrobial resistance is also evidenced. View Full-Text
Keywords: machine learning; clinical data; antibiogram; feature selection; correspondence analysis; culture; antimicrobial resistance; bacteria; intensive care unit machine learning; clinical data; antibiogram; feature selection; correspondence analysis; culture; antimicrobial resistance; bacteria; intensive care unit
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Martínez-Agüero, S.; Mora-Jiménez, I.; Lérida-García, J.; Álvarez-Rodríguez, J.; Soguero-Ruiz, C. Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit. Entropy 2019, 21, 603.

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