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Article

Machine Learning and Antibiotic Management

1
Department of Emergency, Intensive Care Medicine and Anesthesia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
2
Department of Statistical Sciences, Università Sapienza, 00161 Rome, Italy
3
Pediatric Cardiac Intensive Care Unit, Department of Cardiac Surgery, Cardiology and Heart Lung Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
4
Infective Disease Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS-Università Cattolica Del Sacro Cuore, 00168 Rome, Italy
*
Author to whom correspondence should be addressed.
Antibiotics 2022, 11(3), 304; https://doi.org/10.3390/antibiotics11030304
Submission received: 16 January 2022 / Revised: 7 February 2022 / Accepted: 18 February 2022 / Published: 24 February 2022

Abstract

Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.
Keywords: machine learning; fuzzy logic; clustering analysis; antibiotic therapy; intensive care unit machine learning; fuzzy logic; clustering analysis; antibiotic therapy; intensive care unit

Share and Cite

MDPI and ACS Style

Maviglia, R.; Michi, T.; Passaro, D.; Raggi, V.; Bocci, M.G.; Piervincenzi, E.; Mercurio, G.; Lucente, M.; Murri, R. Machine Learning and Antibiotic Management. Antibiotics 2022, 11, 304. https://doi.org/10.3390/antibiotics11030304

AMA Style

Maviglia R, Michi T, Passaro D, Raggi V, Bocci MG, Piervincenzi E, Mercurio G, Lucente M, Murri R. Machine Learning and Antibiotic Management. Antibiotics. 2022; 11(3):304. https://doi.org/10.3390/antibiotics11030304

Chicago/Turabian Style

Maviglia, Riccardo, Teresa Michi, Davide Passaro, Valeria Raggi, Maria Grazia Bocci, Edoardo Piervincenzi, Giovanna Mercurio, Monica Lucente, and Rita Murri. 2022. "Machine Learning and Antibiotic Management" Antibiotics 11, no. 3: 304. https://doi.org/10.3390/antibiotics11030304

APA Style

Maviglia, R., Michi, T., Passaro, D., Raggi, V., Bocci, M. G., Piervincenzi, E., Mercurio, G., Lucente, M., & Murri, R. (2022). Machine Learning and Antibiotic Management. Antibiotics, 11(3), 304. https://doi.org/10.3390/antibiotics11030304

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