Machine Learning for Antibiotic Resistance Prediction: A Prototype Using Off-the-Shelf Techniques and Entry-Level Data to Guide Empiric Antimicrobial Therapy

Georgios Feretzakis; Aikaterini Sakagianni; Evangelos Loupelis; Dimitris Kalles; Nikoletta Skarmoutsou; Maria Martsoukou; Constantinos Christopoulos; Malvina Lada; Stavroula Petropoulou; Aikaterini Velentza; Sophia Michelidou; Rea Chatzikyriakou; Evangelos Dimitrellos

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자료유형: 학술저널

저자정보: Georgios Feretzakis (Hellenic Open University) Aikaterini Sakagianni (Sismanogleio General Hospital) Evangelos Loupelis (Sismanogleio General Hospital) Dimitris Kalles (Hellenic Open University) Nikoletta Skarmoutsou (Sismanogleio General Hospital) Maria Martsoukou (Sismanogleio General Hospital) Constantinos Christopoulos (Sismanogleio General Hospital) Malvina Lada (Sismanogleio General Hospital) Stavroula Petropoulou (Sismanogleio General Hospital) Aikaterini Velentza (Sismanogleio General Hospital) Sophia Michelidou (Sismanogleio General Hospital) Rea Chatzikyriakou (Sismanogleio General Hospital) Evangelos Dimitrellos (Sismanogleio General Hospital)

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제27권 제3호

발행연도: 2021.1

수록면: 214 - 221 (8page)

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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.

#Drug Resistance #Anti-Bacterial Agents #Machine Learning #Artificial Intelligence #Supervised Machine Learning

참고문헌 (12)

참고문헌 신청

Gandra S / 2014 / Economic burden of antibiotic resistance : how much do we really know / Clin Microbiol Infect 20(10):973~980

Potron A / 2015 / Emerging broad-spectrum resistance in Pseudomonas aeruginosa and Acinetobacter baumannii : mechanisms and epidemiology / Int J Antimicrob Agents 45(6):568~585

Metlay JP / 2019 / Diagnosis and treatment of adults with community-acquired pneumonia : an official clinical practice guideline of the American Thoracic Society and Infectious Diseases Society of America / Am J Respir Crit Care Med 200(7):e45~e67

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