Parenting

An AI-Supported Methodology for Identifying Attachment Styles

Identifying attachment styles is important for clinical psychologists interested in better understanding their patients. Traditional methods for identifying attachment styles rely on clinical interviews or questionnaires, which are time-consuming and subject to observer bias. Large Language Models offer potential for automated, objective attachment style analysis. The aim of the paper was to devel...

Dramatic increases in redundant publications in the Generative AI era

Redundant publication, the practice of submitting the same or substantially overlapping manuscripts multiple times, distorts the scientific record and wastes resources. Since 2022, publications using large open-science data resources have increased substantially, raising concerns that Generative AI (GenAI) may be facilitating the production of formulaic, redundant manuscripts. In this work we aim ...

Non-Traditional Lipid Ratios Predict Cardiovascular-Kidney-Metabolic Syndrome: Insights from Machine Learning Model Using NHANES Data

Cardiovascular-kidney-metabolic (CKM) syndrome is a newly defined multisystem disease continuum characterized by the coexistence of metabolic dysfunct...

A Metabolic-Inflammatory Phenotype of Pelvic Floor Dysfunction: A Machine Learning-Based Discovery in a Nationally Representative U.S. Cohort

Pelvic floor dysfunction (PFD) is a highly prevalent and heterogeneous condition among women. The traditional view of PFD as a single clinical entity ...

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration...

FusionAge framework for multimodal machine learning-based aging clocks uncovers cardiorespiratory fitness as a major driver of aging and inflammatory drivers of aging in response to spaceflight

Traditional epigenetic aging clocks are limited because they do not incorporate clinical information and functional tests, and rely on DNA samples and...

Nationwide Spatiotemporal Dynamics and Machine Learning Prediction of Anemia Among Women in Lesotho, 2023–2024

Despite substantial efforts, anemia continues to pose a significant public health challenge, disproportionately affecting women of reproductive age. I...

Multi-domain Identification of Myocardial Infarction Incidence using Explainable AI: The Overlooked Role of Periodontal Health

Myocardial infarction (MI) is a major global health concern influenced by diverse risk factors. Despite growing evidence of oral– systemic connections...

The dynamic linkage between covid-19 and nutrition: a review from a probiotics perspective using machine learning and bibliometric analysis.

INTRODUCTION: The pandemic crisis is now a memorable milestone in the history of science, not only for the impacts on the population's health but also...

Perspective on the ethics of AI at the intersection of nutrition and behaviour change.

Artificial intelligence (AI) has emerged as a powerful tool, that has the potential to impact society on multiple levels. Increased adoption as well a...

The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability.

BACKGROUND: Generative artificial intelligence (AI) chatbots are increasingly utilised in various domains, including sports nutrition. Despite their g...

Living on the Edge: Paradoxical Experiences with Ethics (Reprinted with permission).

Paradox is living phenomenon that provides insights into straight thinking and diverse human experiences important to the discipline of nursing from a...

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as t...

Establishment and Validation of a Machine-Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra-Abdominal Candidiasis in Septic Patients.

This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra-abd...

Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy

Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and an...

Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool

\textbf{G}raph \textbf{N}eural \textbf{N}etworks~(GNNs) have achieved significant success in various real-world applications, including social netwo...

NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning

Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Que...

Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review

This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and eva...

PyPulse: A Python Library for Biosignal Imputation

We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in the...

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model archi...