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

dc.contributor.authorThomas, Diana M.
dc.contributor.authorKleinberg, Samantha
dc.contributor.authorBrown, Andrew
dc.contributor.authorCrow, Mason
dc.contributor.authorBastian, Nathaniel D.
dc.contributor.authorReisweber, Nicholas A.
dc.contributor.authorLasater, Robert
dc.contributor.authorKendall, Thomas
dc.contributor.authorShafto, Patrick
dc.contributor.authorBlaine, Raymond W.
dc.contributor.authorSmith, Sarah
dc.contributor.authorRuiz, Daniel C.
dc.contributor.authorMorrell, Christopher
dc.contributor.authorClark, Nicholas J.
dc.date.accessioned2024-05-01T17:20:11Z
dc.date.available2024-05-01T17:20:11Z
dc.date.issued2022
dc.description.abstractBackground 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.
dc.description.sponsorshipDMT, SK, MC, NC, RB, and CM were supported by NIH U54TR004279. AB was supported by NIH R25DK099080, NIH R25GM141507, and NIH R25HL124208. PS was supported by DARPA XAI FA8750-17-2-0146.
dc.identifier.citationThomas, Diana M., Samantha Kleinberg, Andrew W. Brown, Mason Crow, Nathaniel D. Bastian, Nicholas Reisweber, Robert Lasater, et al. 2022. “Machine Learning Modeling Practices to Support the Principles of AI and Ethics in Nutrition Research.” Nutrition & Diabetes. Springer Science and Business Media LLC. https://doi.org/10.1038/s41387-022-00226-y.
dc.identifier.doihttps://doi.org/10.1038/s41387-022-00226-y
dc.identifier.urihttps://hdl.handle.net/20.500.14216/1478
dc.publisherNutrition & Diabetes
dc.subjectEthics
dc.titleMachine learning modeling practices to support the principles of AI and ethics in nutrition research
dc.typeJournal articles
local.USMAemailnicholas.clark@westpoint.edu
local.peerReviewedYes

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