AIMC Topic: Nutrition Surveys

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Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

The journal of pain
This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults age...

NAIF: A novel artificial intelligence-based tool for accurate diagnosis of stage F3/F4 liver fibrosis in the general adult population, validated with three external datasets.

International journal of medical informatics
OBJECTIVE: The purpose of this study was to determine the effectiveness of a new AI-based tool called NAIF (NAFLD-AI-Fibrosis) in identifying individuals from the general population with advanced liver fibrosis (stage F3/F4). We compared NAIF's perfo...

Association between plain water intake and risk of hypertension: longitudinal analyses from the China Health and Nutrition Survey.

Frontiers in public health
OBJECTIVE: This study aimed to investigate the prospective association between plain water intake and the risk of hypertension based on a longitudinal cohort study in China.

Association between household income levels and nutritional intake of allergic children under 6 years of age in Korea: 2019 Korea National Health and Nutrition Examination Survey and application of machine learning.

Frontiers in public health
INTRODUCTION: This study investigated the prevalence of allergic diseases in Korean children aged 6 and below, focusing on the interplay between nutritional status, household income levels, and allergic disease occurrence.

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study.

Frontiers in public health
OBJECTIVE: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depr...

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

Frontiers in endocrinology
AIMS: This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.

The lancet. Healthy longevity
BACKGROUND: Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framew...

Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018.

BMC psychiatry
BACKGROUND: Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used ...

Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction.

The Laryngoscope
BACKGROUND: Machine learning (ML) analysis of biometric data in non-controlled environments is underexplored.