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Explainable machine learning for predicting childhood anemia in Sub-Saharan Africa using population-based DHS Data (2016-2024).

Childhood anemia remains a major public health challenge in Sub-Saharan Africa, adversely affecting physical growth, cognitive development, and child survival. The pooled prevalence across 26 countries exceeds 60%, underscoring the need for accurate and scalable prediction tools to support targeted interventions.This study used pooled Demographic and Health Survey (DHS) data (2016-2024) from 26 Su...

Digital health in acute care nutrition and dietetics practice: a scoping review.

BACKGROUND: Digital health technologies are transforming healthcare. There is limited research describing how these technologies have been applied by nutrition and dietetic professionals across the nutrition care process. A summary of the literature is needed to guide future research. OBJECTIVE: To examine how digital health technologies have been applied in acute care nutrition and dietetics prac...

Consumer Perceptions of the German Food-Based Dietary Guidelines and the Complementary Potential of AI-Based Nutrition Tools.

Food-based dietary guidelines (FBDGs) are a key instrument for communicating healthy nutrition principles that are implemented in most countries. Howe...

Comment on "Artificial Intelligence in Clinical Nutrition: A Narrative Review".

Decoding how macronutrients influence depressive symptoms: Combined insights from regression, nonlinear modeling, and machine learning: a cross-sectional study using NHANES data.

Dietary management is an essential component of comprehensive depression care;however, studies simultaneously evaluating multiple macronutrients and t...

Legume-based selenium bioavailability for crop and human nutrition: A global meta-analysis.

Selenium (Se) plays an important role in plant growth and human nutrition, and its narrow safety margin can cause adverse effects in the legume-based ...

Artificial Intelligence Meets Oral Medicine: Extending the Capabilities of Human Intelligence.

OBJECTIVES: Oral medicine is an interdisciplinary field that focuses on non-odontogenic diseases. The emergence of artificial intelligence (AI) offers...

Engineering Biology in an Age of Convergence: Evolving Research Leadership at the AI-SynBio-Robotics Frontier.

Artificial intelligence (AI), engineering biology (often called synthetic biology), automation, and RNA therapeutics are rapidly converging into integ...

Spatially resolved tissue architecture and computational pathology in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease characterized by high levels of cellular heterogeneity and pronounced microenvironmental ...

Convergences and divergences in nursing ethics across the European Union: A qualitative comparative analysis of national codes.

BACKGROUND: Nursing ethics guides practice, but European Union national codes were developed in diverse socio-legal contexts, generating heterogeneous...

AI-assisted post-mortem classification of vehicle trauma in free-roaming cats using transformer-based large language models.

Veterinary forensic science is a specialized discipline that investigates non-natural animal deaths and injuries. Determining the cause of death in fr...

Precision pharmacology: deep learning infused ontological framework with E-GRU enhancement for tailored medicine prescriptions.

Advanced Clinical Decision Support Systems significantly influence patient care, with medicine prescriptions being a vital area of research. Ontology,...

A machine learning-based framework for individualized physical activity and strength targets related to women's bone health.

While substantial evidence supports the associations between physical activity and bone health, the present study aims to advance the field by applyin...

Meeting the 24-Hour Movement Guidelines and Frailty Risk: Evidence From Two Nationally Representative Population Studies and Machine-Learning Models.

AIM: This study aimed to examine the association between meeting the 24-Hour Movement Guideline components-moderate-to-vigorous physical activity (MVP...

Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.

Artificial intelligence (AI) is transforming food-nutrition-health research by enabling pattern recognition in complex, high-dimensional datasets that...

Integrating gut‑brain axis insights into bundled nutrition care for critically ill patients: From mechanisms to implementation (Review).

Critical illness induces a marked disruption of the gut‑brain axis, which is characterized by systemic inflammation and the rapid collapse of intestin...

Residential pesticide exposure, mortality risk, and the potential mediating role of urinary metabolites: evidence from a nationally representative cohort.

Global warming has increased the risk of vector-borne diseases, leading to more widespread pesticide use. While health effects of occupational or agri...

Identifying the Most Important Influencing Factors Based on Demographic Information, Nutrition, Physical Activity, Leisure Time, and Underlying Diseases in the Severity of Disease in COVID-19 Patients Using Data Mining Techniques.

BACKGROUND AND AIMS: The COVID-19 pandemic has affected millions of individuals worldwide and resulted in substantial mortality. Data mining and machi...

Attitudes and Usage of ChatGPT Among Medical and Paramedical Students in Iran: A Cross-Sectional Study.

BACKGROUND AND AIMS: ChatGPT, an AI-powered conversational tool, has shown potential to transform healthcare education. However, its integration poses...

Interpretable Machine Learning for Population-Level Tooth Loss Prediction.

Machine learning can support population-level severe tooth loss (STL; ≥6 missing teeth) risk stratification; however, a lack of calibration under doma...