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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-8944459 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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