ObjectiveBig Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)‐ and ML‐ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.MethodsWe reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI‐ and ML‐ready products were detailed.ResultsOpportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.ConclusionsBig Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI‐ and ML‐ready data need to be transparent to investigators and clinicians relying on the conclusions.

中文翻译：

---

将大数据转化为人工智能数据，用于营养和肥胖研究


Objective大数据越来越多地应用于肥胖和营养研究，以获得新的见解并得出个性化指导；然而，这些原始数据通常无法使用。将大数据转换为人工智能 (AI) 和 ML 就绪数据需要进行大量预处理，这需要机器学习 (ML)、人类判断和专用软件。这些预处理步骤是整个建模流程中最复杂的部分。最终用户了解这些步骤的复杂性对于减少误解、错误解释和错误的下游结论至关重要。方法我们回顾了三种流行的肥胖/营养大数据源：微生物组、代谢组学和加速测量。详细介绍了预处理流程、专用软件、挑战以及决策如何影响最终的 AI 和 ML 就绪产品。结果提出了改进质量控制、预处理速度和智能最终用户消费的机会。结论大数据具有令人兴奋的潜力用于识别影响肥胖研究的新的可改变因素。然而，为了确保准确解释大数据得出的结论，准备人工智能和机器学习数据所涉及的选择需要对依赖结论的研究人员和临床医生透明。