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Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review
Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially deliver...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229087/ https://www.ncbi.nlm.nih.gov/pubmed/32048244 http://dx.doi.org/10.1007/s41030-020-00110-z |
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author | Mlodzinski, Eric Stone, David J. Celi, Leo A. |
author_facet | Mlodzinski, Eric Stone, David J. Celi, Leo A. |
author_sort | Mlodzinski, Eric |
collection | PubMed |
description | Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes. |
format | Online Article Text |
id | pubmed-7229087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-72290872020-05-18 Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review Mlodzinski, Eric Stone, David J. Celi, Leo A. Pulm Ther Review Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes. Springer Healthcare 2020-02-05 /pmc/articles/PMC7229087/ /pubmed/32048244 http://dx.doi.org/10.1007/s41030-020-00110-z Text en © The Author(s) 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any non-commercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Review Mlodzinski, Eric Stone, David J. Celi, Leo A. Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review |
title | Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review |
title_full | Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review |
title_fullStr | Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review |
title_full_unstemmed | Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review |
title_short | Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review |
title_sort | machine learning for pulmonary and critical care medicine: a narrative review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229087/ https://www.ncbi.nlm.nih.gov/pubmed/32048244 http://dx.doi.org/10.1007/s41030-020-00110-z |
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