Parenting

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model based on machine learning (ML) approaches for MASLD among the adult population. The national cross-sectional study collected data from the National He...

The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.

The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. Machine learning (ML) algorithms were also selected aimed at providing predictions to suggest practical recommendations for public awareness and clinical treatment. We included 21,279 eligible participants from t...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few stu...

Supporting Speech-Language Pathologists in Schools With Interdisciplinary Team Science: A Viewpoint From the National Artificial Intelligence Institute for Exceptional Education.

PURPOSE: Complex scientific problems, including those facing the discipline of communication sciences and disorders (CSD), require interdisciplinary t...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth re...

The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications.

Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and su...

Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding.

Traditional feeding strategy during lactation can result in nutrient deficiencies and negatively impact long-term productivity, compromising both the ...

Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient mach...

AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.

Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven ...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permane...

Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration.

This review explores the health benefits of bioactive peptides (BAPs) from fermented milk proteins, emphasizing the transformative role of artificial ...

Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children.

BACKGROUND: Child nutrition in Ethiopia is a significant concern, particularly for preschool-aged children. Children must have a varied diet to ensure...

AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters.

Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex d...

Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.

Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely interventio...

Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.

BACKGROUND: Dietary supplements (DSs) are widely used to improve health and nutrition, but challenges related to misinformation, safety, and efficacy ...

Life's Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Cardiovascular disease (CVD) and NAFLD share multiple...

Assessing the Links Between Artificial Intelligence and Precision Nutrition.

PURPOSE OF REVIEW: To conduct an overview of the potentialities of artificial intelligence in precision nutrition.

Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.

BACKGROUND: The multifaceted issue of childhood stunting in low- and middle-income countries has a profound and enduring impact on children's well-bei...

AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed.

Recent developments in artificial intelligence (AI) have seen a proliferation of algorithms that are now capable of predicting a range of systemic dis...