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
Department of Mathematical Sciences, United States Military Academy, West Point, NY, 10996, USA.
Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
Nutr Diabetes. 2022 Dec 2;12(1):48. doi: 10.1038/s41387-022-00226-y.
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.
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.
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.
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.
营养研究越来越依赖人工智能和机器学习模型来理解、诊断、预测和解释数据。虽然人工智能和机器学习模型提供了强大的建模工具，但未能使用谨慎且经过深思熟虑的建模过程可能会导致误导性结论以及围绕伦理和偏差的问题。
基于我们作为营养与肥胖领域的审稿人和期刊编辑的经验，我们确定了统计建模中最常被遗漏的最佳实践，以及这些实践如何同样适用于机器学习模型。接下来，我们讨论了商业软件包中未包含的机器学习实施所需的领域。
在此，我们提供了一份关于最佳人工智能和机器学习建模实践的教程，其中包含一份清单和指导原则，可减少潜在的伦理问题，以帮助营养研究人员在营养研究中开发、评估和实施人工智能和机器学习模型。
营养研究中人工智能/机器学习建模的质量需要迭代和量身定制的过程，以减轻潜在的伦理问题或预测无偏差的结论。
