Machine Learning in Nutrition Research (2022-09-27T00:00:00.000000Z)

TL;DR

This article aims to bridge the knowledge gap in the field of nutrition by supplying nutrition researchers with a resource to facilitate the use of machine learning, and outlines a framework to guide interested researchers in integrating ML into their work.

Abstract

ABSTRACT Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Authors

Daniel Kirk

1 papers

Esther Kok

1 papers

Michele Tufano

1 papers

References168 items

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

TL;DR

Abstract

Authors

References168 items

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