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Machine Learning and Antibiotic Management

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 evalu...

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Autores principales: Maviglia, Riccardo, Michi, Teresa, Passaro, Davide, Raggi, Valeria, Bocci, Maria Grazia, Piervincenzi, Edoardo, Mercurio, Giovanna, Lucente, Monica, Murri, Rita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944459/
https://www.ncbi.nlm.nih.gov/pubmed/35326768
http://dx.doi.org/10.3390/antibiotics11030304
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author Maviglia, Riccardo
Michi, Teresa
Passaro, Davide
Raggi, Valeria
Bocci, Maria Grazia
Piervincenzi, Edoardo
Mercurio, Giovanna
Lucente, Monica
Murri, Rita
author_facet Maviglia, Riccardo
Michi, Teresa
Passaro, Davide
Raggi, Valeria
Bocci, Maria Grazia
Piervincenzi, Edoardo
Mercurio, Giovanna
Lucente, Monica
Murri, Rita
author_sort Maviglia, Riccardo
collection PubMed
description 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.
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spelling pubmed-89444592022-03-25 Machine Learning and Antibiotic Management Maviglia, Riccardo Michi, Teresa Passaro, Davide Raggi, Valeria Bocci, Maria Grazia Piervincenzi, Edoardo Mercurio, Giovanna Lucente, Monica Murri, Rita Antibiotics (Basel) Article 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. MDPI 2022-02-24 /pmc/articles/PMC8944459/ /pubmed/35326768 http://dx.doi.org/10.3390/antibiotics11030304 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Maviglia, Riccardo
Michi, Teresa
Passaro, Davide
Raggi, Valeria
Bocci, Maria Grazia
Piervincenzi, Edoardo
Mercurio, Giovanna
Lucente, Monica
Murri, Rita
Machine Learning and Antibiotic Management
title Machine Learning and Antibiotic Management
title_full Machine Learning and Antibiotic Management
title_fullStr Machine Learning and Antibiotic Management
title_full_unstemmed Machine Learning and Antibiotic Management
title_short Machine Learning and Antibiotic Management
title_sort machine learning and antibiotic management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944459/
https://www.ncbi.nlm.nih.gov/pubmed/35326768
http://dx.doi.org/10.3390/antibiotics11030304
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