Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece

  02 February 2020

In a single center study, the authors compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient’s clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.


Further reading: Antibiotics
Author(s): Georgios Feretzakis, Evangelos Loupelis, Aikaterini Sakagianni, Dimitris Kalles, Maria Martsoukou, Malvina Lada, Nikoletta Skarmoutsou, Constantinos Christopoulos, Konstantinos Valakis, Aikaterini Velentza, Stavroula Petropoulou, Sophia Michelidou and Konstantinos Alexiou
Smart Innovations  


Unrestricted financial support by:


Antimicrobial Resistance Fighter Coalition

Bangalore Bioinnovation Centre


EADA 2023

Emerging Antimicrobials and Diagnostics in AMR 2023

International Matchmaking Symposium EADA 2023
Amsterdam, The Netherlands
16/17 November

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