Machine Learning for Antibiotic Resistance Prediction: A Prototype Using Off-the-Shelf Techniques and Entry-Level Data to Guide Empiric Antimicrobial Therapy
| Autor: | Evangelos Loupelis, Rea Chatzikyriakou, Evangelos Dimitrellos, Aikaterini Sakagianni, Georgios Feretzakis, Malvina Lada, Stavroula Petropoulou, Dimitris Kalles, Constantinos Christopoulos, Nikoletta Skarmoutsou, Sophia Michelidou, Aikaterini Velentza, Maria Martsoukou |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.drug_class
Computer applications to medicine. Medical informatics Entry Level Antibiotics R858-859.7 Biomedical Engineering Health Informatics Drug resistance Machine learning computer.software_genre Clinical decision support system anti-bacterial agents Antibiotic resistance Health Information Management Off the shelf Medicine supervised machine learning drug resistance business.industry artificial intelligence Antimicrobial machine learning Original Article Metric (unit) Artificial intelligence business computer |
| Zdroj: | Healthcare Informatics Research, Vol 27, Iss 3, Pp 214-221 (2021) Healthcare Informatics Research |
| ISSN: | 2093-369X |
| DOI: | 10.4258/hir.2021.27.3.214 |
| Popis: | Objectives: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As traditional microbiological susceptibility testing requires at least 24 hours, automated machine learning (AutoML) techniques could be used as clinical decision support tools to predict antimicrobial resistance and select appropriate empirical antibiotic treatment.Methods: An antimicrobial susceptibility dataset of 11,496 instances from 499 patients admitted to the internal medicine wards of a public hospital in Greece was processed by using Microsoft Azure AutoML to evaluate antibiotic susceptibility predictions using patients’ simple demographic characteristics, as well as previous antibiotic susceptibility testing, without any concomitant clinical data. Furthermore, the balanced dataset was also processed using the same procedure. The datasets contained the attributes of sex, age, sample type, Gram stain, 44 antimicrobial substances, and the antibiotic susceptibility results.Results: The stack ensemble technique achieved the best results in the original and balanced dataset with an area under the curve-weighted metric of 0.822 and 0.850, respectively.Conclusions: Implementation of AutoML for antimicrobial susceptibility data can provide clinicians useful information regarding possible antibiotic resistance and aid them in selecting appropriate empirical antibiotic therapy by taking into consideration the local antimicrobial resistance ecosystem. |
| Databáze: | OpenAIRE |
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