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Machine learning modeling practices to support the principles of AI and ethics in nutrition research

BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling pr...

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Autores principales: Thomas, Diana M., Kleinberg, Samantha, Brown, Andrew W., Crow, Mason, Bastian, Nathaniel D., Reisweber, Nicholas, Lasater, Robert, Kendall, Thomas, Shafto, Patrick, Blaine, Raymond, Smith, Sarah, Ruiz, Daniel, Morrell, Christopher, Clark, Nicholas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715415/
https://www.ncbi.nlm.nih.gov/pubmed/36456550
http://dx.doi.org/10.1038/s41387-022-00226-y
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author Thomas, Diana M.
Kleinberg, Samantha
Brown, Andrew W.
Crow, Mason
Bastian, Nathaniel D.
Reisweber, Nicholas
Lasater, Robert
Kendall, Thomas
Shafto, Patrick
Blaine, Raymond
Smith, Sarah
Ruiz, Daniel
Morrell, Christopher
Clark, Nicholas
author_facet Thomas, Diana M.
Kleinberg, Samantha
Brown, Andrew W.
Crow, Mason
Bastian, Nathaniel D.
Reisweber, Nicholas
Lasater, Robert
Kendall, Thomas
Shafto, Patrick
Blaine, Raymond
Smith, Sarah
Ruiz, Daniel
Morrell, Christopher
Clark, Nicholas
author_sort Thomas, Diana M.
collection PubMed
description BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias. METHODS: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages. RESULTS: Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research. CONCLUSION: The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.
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spelling pubmed-97154152022-12-02 Machine learning modeling practices to support the principles of AI and ethics in nutrition research Thomas, Diana M. Kleinberg, Samantha Brown, Andrew W. Crow, Mason Bastian, Nathaniel D. Reisweber, Nicholas Lasater, Robert Kendall, Thomas Shafto, Patrick Blaine, Raymond Smith, Sarah Ruiz, Daniel Morrell, Christopher Clark, Nicholas Nutr Diabetes Article BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias. METHODS: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages. RESULTS: Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research. CONCLUSION: The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias. Nature Publishing Group UK 2022-12-02 /pmc/articles/PMC9715415/ /pubmed/36456550 http://dx.doi.org/10.1038/s41387-022-00226-y Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 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
Thomas, Diana M.
Kleinberg, Samantha
Brown, Andrew W.
Crow, Mason
Bastian, Nathaniel D.
Reisweber, Nicholas
Lasater, Robert
Kendall, Thomas
Shafto, Patrick
Blaine, Raymond
Smith, Sarah
Ruiz, Daniel
Morrell, Christopher
Clark, Nicholas
Machine learning modeling practices to support the principles of AI and ethics in nutrition research
title Machine learning modeling practices to support the principles of AI and ethics in nutrition research
title_full Machine learning modeling practices to support the principles of AI and ethics in nutrition research
title_fullStr Machine learning modeling practices to support the principles of AI and ethics in nutrition research
title_full_unstemmed Machine learning modeling practices to support the principles of AI and ethics in nutrition research
title_short Machine learning modeling practices to support the principles of AI and ethics in nutrition research
title_sort machine learning modeling practices to support the principles of ai and ethics in nutrition research
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715415/
https://www.ncbi.nlm.nih.gov/pubmed/36456550
http://dx.doi.org/10.1038/s41387-022-00226-y
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