Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece
School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
IT Department, Sismanogleio General Hospital, 15126 Marousi, Greece
Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, 15126 Marousi, Greece
Intensive Care Unit, Sismanogleio General Hospital, 15126 Marousi, Greece
Microbiology Laboratory, Sismanogleio General Hospital, 15126 Marousi, Greece
2nd Internal Medicine Department, Sismanogleio General Hospital, 15126 Marousi, Greece
1st Internal Medicine Department, Sismanogleio General Hospital, 15126 Marousi, Greece
1st Surgery Department, Sismanogleio General Hospital, 15126 Marousi, Greece
Author to whom correspondence should be addressed.
Antibiotics 2020, 9(2), 50; https://doi.org/10.3390/antibiotics9020050
Received: 8 January 2020 / Revised: 26 January 2020 / Accepted: 27 January 2020 / Published: 31 January 2020
(This article belongs to the Section Antibiotics Use and Antimicrobial Stewardship)
Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample’s Gram stain, site of infection, and patient demographics. In our single center study, we 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.