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Data science in the intensive care unit
In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial in...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9483002/ https://www.ncbi.nlm.nih.gov/pubmed/36160936 http://dx.doi.org/10.5492/wjccm.v11.i5.311 |
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author | Luo, Ming-Hao Huang, Dan-Lei Luo, Jing-Chao Su, Ying Li, Jia-Kun Tu, Guo-Wei Luo, Zhe |
author_facet | Luo, Ming-Hao Huang, Dan-Lei Luo, Jing-Chao Su, Ying Li, Jia-Kun Tu, Guo-Wei Luo, Zhe |
author_sort | Luo, Ming-Hao |
collection | PubMed |
description | In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI. |
format | Online Article Text |
id | pubmed-9483002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-94830022022-09-23 Data science in the intensive care unit Luo, Ming-Hao Huang, Dan-Lei Luo, Jing-Chao Su, Ying Li, Jia-Kun Tu, Guo-Wei Luo, Zhe World J Crit Care Med Editorial In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI. Baishideng Publishing Group Inc 2022-09-09 /pmc/articles/PMC9483002/ /pubmed/36160936 http://dx.doi.org/10.5492/wjccm.v11.i5.311 Text en ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Editorial Luo, Ming-Hao Huang, Dan-Lei Luo, Jing-Chao Su, Ying Li, Jia-Kun Tu, Guo-Wei Luo, Zhe Data science in the intensive care unit |
title | Data science in the intensive care unit |
title_full | Data science in the intensive care unit |
title_fullStr | Data science in the intensive care unit |
title_full_unstemmed | Data science in the intensive care unit |
title_short | Data science in the intensive care unit |
title_sort | data science in the intensive care unit |
topic | Editorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9483002/ https://www.ncbi.nlm.nih.gov/pubmed/36160936 http://dx.doi.org/10.5492/wjccm.v11.i5.311 |
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