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Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit
BACKGROUND: Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify. METHODS: We begin with a q...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768782/ https://www.ncbi.nlm.nih.gov/pubmed/36543940 http://dx.doi.org/10.1038/s43856-022-00225-1 |
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author | D’Hondt, Ellie Ashby, Thomas J. Chakroun, Imen Koninckx, Thomas Wuyts, Roel |
author_facet | D’Hondt, Ellie Ashby, Thomas J. Chakroun, Imen Koninckx, Thomas Wuyts, Roel |
author_sort | D’Hondt, Ellie |
collection | PubMed |
description | BACKGROUND: Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify. METHODS: We begin with a qualitative study with 29 interviews of 40 Intensive Care Unit-, hospital- and MedTech company staff members. As a follow-up to the study, we attempt to quantify some of the technical issues raised. To perform experiments we selected two models based on criteria such as medical relevance. Using these models we measure the loss of performance in predictive models due to drift over time, change of available patient features, scarceness of data, and deploying a model in a different context to the one it was built in. RESULTS: The qualitative study confirms our assumptions on the potential of AI-driven analytics for patient care, as well as showing the prevalence and type of technical blocking factors that are responsible for its slow uptake. The experiments confirm that each of these issues can cause important loss of predictive model performance, depending on the model and the issue. CONCLUSIONS: Based on the qualitative study and quantitative experiments we conclude that more research on practical solutions to enable AI-driven innovation in Intensive Care Units is needed. Furthermore, the general poor situation with respect to public, usable implementations of predictive models would appear to limit the possibilities for both the scientific repeatability of the underlying research and the transfer of this research into practice. |
format | Online Article Text |
id | pubmed-9768782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97687822022-12-21 Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit D’Hondt, Ellie Ashby, Thomas J. Chakroun, Imen Koninckx, Thomas Wuyts, Roel Commun Med (Lond) Article BACKGROUND: Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify. METHODS: We begin with a qualitative study with 29 interviews of 40 Intensive Care Unit-, hospital- and MedTech company staff members. As a follow-up to the study, we attempt to quantify some of the technical issues raised. To perform experiments we selected two models based on criteria such as medical relevance. Using these models we measure the loss of performance in predictive models due to drift over time, change of available patient features, scarceness of data, and deploying a model in a different context to the one it was built in. RESULTS: The qualitative study confirms our assumptions on the potential of AI-driven analytics for patient care, as well as showing the prevalence and type of technical blocking factors that are responsible for its slow uptake. The experiments confirm that each of these issues can cause important loss of predictive model performance, depending on the model and the issue. CONCLUSIONS: Based on the qualitative study and quantitative experiments we conclude that more research on practical solutions to enable AI-driven innovation in Intensive Care Units is needed. Furthermore, the general poor situation with respect to public, usable implementations of predictive models would appear to limit the possibilities for both the scientific repeatability of the underlying research and the transfer of this research into practice. Nature Publishing Group UK 2022-12-21 /pmc/articles/PMC9768782/ /pubmed/36543940 http://dx.doi.org/10.1038/s43856-022-00225-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article D’Hondt, Ellie Ashby, Thomas J. Chakroun, Imen Koninckx, Thomas Wuyts, Roel Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
title | Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
title_full | Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
title_fullStr | Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
title_full_unstemmed | Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
title_short | Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
title_sort | identifying and evaluating barriers for the implementation of machine learning in the intensive care unit |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768782/ https://www.ncbi.nlm.nih.gov/pubmed/36543940 http://dx.doi.org/10.1038/s43856-022-00225-1 |
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