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Deep Neural Networks for Image-Based Dietary Assessment
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Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.

Jason D Morgenstern1, Laura C Rosella2,3, Andrew P Costa1

  • 1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.

Advances in Nutrition (Bethesda, Md.)
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

Big data and machine learning offer new ways to improve dietary measurements and model complex dietary patterns. These advanced methods can help address challenges like measurement error and confounding in nutritional epidemiology.

Keywords:
artificial intelligencebig datadietmachine learningnutritionnutritional epidemiologynutritional sciencesprecision nutrition

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Area of Science:

  • Nutritional Epidemiology
  • Data Science
  • Computational Biology

Background:

  • Nutritional epidemiology faces significant challenges including measurement error, the complexity of diet as an exposure, and residual confounding.
  • Current methods for assessing dietary intake and patterns may not fully capture the intricate variations and interactions inherent in human diets.

Purpose of the Study:

  • To explore how advancements in big data and machine learning can address key challenges in nutritional epidemiology.
  • To highlight novel approaches for dietary measurement, modeling complex dietary exposures, and mitigating confounding.

Main Methods:

  • Leveraging new data collection methods for 24-hour dietary recalls and food recording to enhance sample size and precision.
  • Utilizing machine learning for automated food image classification to improve dietary measurement validity.
  • Applying machine learning to model complex dietary patterns, including non-linear and non-additive associations.
  • Integrating novel big data sources and machine learning to identify additional covariates and address unmeasured confounding.

Main Results:

  • Potential for increased statistical power and measurement precision through larger samples and repeated dietary measures.
  • Machine learning offers enhanced capabilities for modeling diet's complexity and its associations with health outcomes.
  • Big data sources combined with machine learning can provide more comprehensive covariate data to reduce confounding.

Conclusions:

  • Judicious application of big data and machine learning can provide innovative tools for dietary assessment and analysis in nutrition science.
  • These technologies offer new avenues for understanding diet-disease relationships and improving epidemiological study designs.
  • Careful implementation is crucial, requiring attention to data quality, model validation, and necessary investments in training and infrastructure.