Machine learning algorithms for the prediction of bacterial resistance
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1264Keywords:
Intensive Care Units, Machine Learning, Microbial drug resistanceAbstract
Antibiotic resistance represents a significant concern for global health, particularly in intensive care units (ICUs), where rapid diagnosis is essential. Study objective: To test Machine Learning algorithms for predicting bacterial resistance in ICUs; Methods: Factors such as age, gender, sample type, tested antibiotic, and Gram staining of bacteria were extracted from the MIMIC-III database and used for training six machine learning models. Results: The Extreme Gradient Boosting showed the highest prediction accuracy, at 84.53%. Conclusion: Machine Learning could offer a solution for the early detection of antibiotic resistance, thereby improving patient care and antibiotic management.
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Feretzakis G, Loupelis E, Sakagianni A, Kalles D, Martsoukou M, Lada M, et al. Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece. Antibiot 2020, Vol 9, Page 50
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