ArticleThe American journal of clinical nutrition2020
Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes.
Article in The American journal of clinical nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 61 citations in OpenAlex.
- Interpersonal Communication and Maternal Behavioral Practice: A Case Study of Explanatory Causal Machine Learning in Nepal.The Journal of nutrition · 2026Article
- From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes.The British journal of nutrition · 2026Article
- Micronutrients as Mitigators of Endocrine Disrupting Chemical Health Effects: A Scoping Review and Framework for Epidemiologic Studies.Current environmental health reports · 2026Article
- A comparative study of vision-language models for food ingredient recognition and nutrient estimation.Current research in food science · 2026Article
- Predicting diet quality and food consumption at eating occasions using contextual factors: an application of machine learning models.The international journal of behavioral nutrition and physical activity · 2025Article
- Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be.Epidemiology (Cambridge, Mass.) · 2025Article
- Unveiling Dietary Complexity: A Scoping Review and Reporting Guidance for Network Analysis in Dietary Pattern Research.Nutrients · 2025Article
- Heterogeneity in the association between a dietary pattern high in fat, sugar, and sodium and adverse pregnancy outcomes by maternal characteristics: a United States pregnancy cohort study.The American journal of clinical nutrition · 2025Article
- Article
- Challenges for Predictive Modeling With Neural Network Techniques Using Error-Prone Dietary Intake Data.Statistics in medicine · 2025Article
- Maternal Preconception Omega-6, Omega-3, and Omega-6:Omega-3 Intake and Uterine Artery Indices in Mid-Gestation.American journal of perinatology · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Periconceptional Dietary Quality and Metabolic Syndrome at 3 Years Postpartum.Journal of the American Heart Association · 2024Article
- Advances in methods for characterizing dietary patterns: A scoping review.medRxiv : the preprint server for health sciences · 2024Article
- Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes.The Journal of nutrition · 2024Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology.Annals of epidemiology · 2023Article
- Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications.Frontiers in nutrition · 2023Review
- Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients.Frontiers in nutrition · 2023Article
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Frontiers in endocrinology · 2023Review
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Authors and funding
11 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundConventional analytic approaches for studying diet patterns assume no dietary synergy, which can lead to bias if incorrectly modeled. Machine learning algorithms can overcome these limitations.
objectivesWe estimated associations between fruit and vegetable intake relative to total energy intake and adverse pregnancy outcomes using targeted maximum likelihood estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared these with results generated from multivariable logistic regression.
methodsWe used data from 7572 women in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be. Usual daily periconceptional intake of total fruits and total vegetables was estimated from an FFQ. We calculated the marginal risk of preterm birth, small-for-gestational-age (SGA) birth, gestational diabetes, and pre-eclampsia according to density of fruits and vegetables (cups/1000 kcal) ≥80th percentile compared with <80th percentile using multivariable logistic regression and Super Learner with TMLE. Models were adjusted for confounders, including other Healthy Eating Index-2010 components.
resultsUsing logistic regression, higher fruit and high vegetable densities were associated with 1.1% and 1.4% reductions in pre-eclampsia risk compared with lower densities, respectively. They were not associated with the 3 other outcomes. Using Super Learner with TMLE, high fruit and vegetable densities were associated with fewer cases of preterm birth (-4.0; 95% CI: -4.9, -3.0 and -3.7; 95% CI: -5.0, -2.3), SGA (-1.7; 95% CI: -2.9, -0.51 and -3.8; 95% CI: -5.0, -2.5), and pre-eclampsia (-3.2; 95% CI: -4.2, -2.2 and -4.0; 95% CI: -5.2, -2.7) per 100 births, respectively, and high vegetable densities were associated with a 0.9% increase in risk of gestational diabetes.
conclusionsThe differences in results between Super Learner with TMLE and logistic regression suggest that dietary synergy, which is accounted for in machine learning, may play a role in pregnancy outcomes. This innovative methodology for analyzing dietary data has the potential to advance the study of diet patterns.
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