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

Thomas, Diana M.; Kleinberg, Samantha; Brown, Andrew W.; Crow, Mason; Bastian, Nathaniel D.; Reisweber, Nicholas; Lasater, Robert; Kendall, Thomas; Shafto, Patrick; Blaine, R. W.; Smith, Sarah C.; Ruiz, Daniel C.; Morrell, Christopher H.; Clark, Nicholas J.

doi:10.1038/s41387-022-00226-y

Cited by 14 publications

(8 citation statements)

References 75 publications

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“…Thomas et al . [2 ▪▪ ] offer a lucid and practical guide to implementing machine learning approaches. Their work focuses on gaining important insights whilst avoiding the ethical pitfalls of previous attempts to analyse big data.…”

Section: Opportunities and Risks In Machine Learning For Paediatric N...mentioning

confidence: 99%

The use of machine learning in paediatric nutrition

Young,

Johnson,

Beattie

2024

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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Purpose of review In recent years, there has been a burgeoning interest in using machine learning methods. This has been accompanied by an expansion in the availability and ease of use of machine learning tools and an increase in the number of large, complex datasets which are suited to machine learning approaches. This review summarizes recent work in the field and sets expectations for its impact in the future. Recent findings Much work has focused on establishing good practices and ethical frameworks to guide the use of machine learning in research. Machine learning has an established role in identifying features in ‘omics’ research and is emerging as a tool to generate predictive models to identify people at risk of disease and patients at risk of complications. They have been used to identify risks for malnutrition and obesity. Machine learning techniques have also been used to develop smartphone apps to track behaviour and provide healthcare advice. Summary Machine learning techniques are reaching maturity and their impact on observational data analysis and behaviour change will come to fruition in the next 5 years. A set of standards and best practices are emerging and should be implemented by researchers and publishers.

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Section: Opportunities and Risks In Machine Learning For Paediatric N...mentioning

confidence: 99%

The use of machine learning in paediatric nutrition

Young,

Johnson,

Beattie

2024

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

View full text Add to dashboard Cite

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“…Finally, failure to account for the true cost of the labor and focused attention required for quality Big Data preprocessing can result in substandard AI/ML models that are open to error [30].…”

Section: What Are Ai-ready Data?mentioning

confidence: 99%

“…In most cases, AI/ML models require large sample sizes [30]. The required sample size is a function of the model complexity.…”

Section: Integration Of Big Data Into An Ai/ml Modelmentioning

confidence: 99%

“…Raw accelerometer signals may also be used directly as inputs into ML models (e.g., deep neural networks) [82]. In the case in which ML is used to transform raw accelerometer data, human judgment needs to be used to correctly label the input data and evaluate output data [30]. In the case in which a package such as GGIR is used, human decisions are made in order to set parameters.…”

Section: Big Data Examples In Obesity and Nutrition Researchmentioning

confidence: 99%

“…When consumers of the AI/ML models do not review the decisions and processes involved for transforming raw data into AI‐ready data, misunderstanding and faulty interpretation of the analysis and conclusions can result downstream. Finally, failure to account for the true cost of the labor and focused attention required for quality Big Data preprocessing can result in substandard AI/ML models that are open to error [30].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transforming Big Data into AI‐ready data for nutrition and obesity research

Thomas,

Knight,

Gilbert

et al. 2024

Obesity

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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.

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Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Soares Dias Portela,

Saxena,

Rosenn

et al. 2024

Molecular Nutrition Food Res

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Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decline. Considering the morbidity associated with AD, it is important to explore lifestyle elements influencing the chances of developing AD, with special emphasis on nutritional aspects. This review will first discuss how dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition. The Mediterranean‐DASH diets provide individuals with many nutrient benefits which assists the prevention of neurodegeneration by having neuroprotective roles. Lack of micronutrients, protein‐energy, and polyunsaturated fatty acids increase the chance of cognitive decline, loss of memory, and synaptic dysfunction among others.ML software has the ability to design models of algorithms from data introduced to present practical solutions that are accessible and easy to use. It can give predictions for a precise medicine approach to evaluate individuals as a whole. There is no doubt the future of nutritional science lies on customizing diets for individuals to reduce dementia risk factors, maintain overall health and brain function.

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

Cited by 14 publications

References 75 publications

The use of machine learning in paediatric nutrition

The use of machine learning in paediatric nutrition

Transforming Big Data into AI‐ready data for nutrition and obesity research

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

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