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Methodology minute: a machine learning primer for infection prevention and control
The use of machine-learning and predictive modeling in infection prevention and control activities is increasing dramatically. In order for infection preventionists to make informed decisions on the performance of any particular model as well as to determine if the output of the model will be useful...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528905/ https://www.ncbi.nlm.nih.gov/pubmed/33011334 http://dx.doi.org/10.1016/j.ajic.2020.09.009 |
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author | Wiemken, Timothy L. Rutschman, Ana Santos |
author_facet | Wiemken, Timothy L. Rutschman, Ana Santos |
author_sort | Wiemken, Timothy L. |
collection | PubMed |
description | The use of machine-learning and predictive modeling in infection prevention and control activities is increasing dramatically. In order for infection preventionists to make informed decisions on the performance of any particular model as well as to determine if the output of the model will be useful for their program needs, a suitable understanding of the creation and evaluation of these models is necessary. The purpose of this primer is to introduce the infection preventionist to the most commonly used machine-learning method in infection prevention: supervised learning. |
format | Online Article Text |
id | pubmed-7528905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75289052020-10-02 Methodology minute: a machine learning primer for infection prevention and control Wiemken, Timothy L. Rutschman, Ana Santos Am J Infect Control Practice Forum The use of machine-learning and predictive modeling in infection prevention and control activities is increasing dramatically. In order for infection preventionists to make informed decisions on the performance of any particular model as well as to determine if the output of the model will be useful for their program needs, a suitable understanding of the creation and evaluation of these models is necessary. The purpose of this primer is to introduce the infection preventionist to the most commonly used machine-learning method in infection prevention: supervised learning. Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. 2020-12 2020-10-01 /pmc/articles/PMC7528905/ /pubmed/33011334 http://dx.doi.org/10.1016/j.ajic.2020.09.009 Text en © 2020 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Practice Forum Wiemken, Timothy L. Rutschman, Ana Santos Methodology minute: a machine learning primer for infection prevention and control |
title | Methodology minute: a machine learning primer for infection prevention and control |
title_full | Methodology minute: a machine learning primer for infection prevention and control |
title_fullStr | Methodology minute: a machine learning primer for infection prevention and control |
title_full_unstemmed | Methodology minute: a machine learning primer for infection prevention and control |
title_short | Methodology minute: a machine learning primer for infection prevention and control |
title_sort | methodology minute: a machine learning primer for infection prevention and control |
topic | Practice Forum |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528905/ https://www.ncbi.nlm.nih.gov/pubmed/33011334 http://dx.doi.org/10.1016/j.ajic.2020.09.009 |
work_keys_str_mv | AT wiemkentimothyl methodologyminuteamachinelearningprimerforinfectionpreventionandcontrol AT rutschmananasantos methodologyminuteamachinelearningprimerforinfectionpreventionandcontrol |