AIMC Topic: Obesity
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Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.
Journal of the American Medical Informatics Association : JAMIA
Oct 21, 2014
OBJECTIVE: The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. ...
Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.
Computer methods and programs in biomedicine
Oct 16, 2014
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS an...
Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.
European journal of clinical investigation
Aug 1, 2025
BACKGROUND: Most forms of obesity are associated with chronic diseases that remain a global public health challenge.
Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis.
International journal of medical informatics
Jul 1, 2025
BACKGROUND: Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning (ML) methods are increasingly popular.
Assessment of Image Quality of Coronary Computed Tomography Angiography in Obese Patients by Comparing Deep Learning Image Reconstruction With Adaptive Statistical Iterative Reconstruction Veo.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of the study was to evaluate the image quality of coronary computed tomography (CT) angiography (CCTA) in obese patients by using deep learning image reconstruction (DLIR) in comparison with adaptive statistical iterative reconstru...
Assessing online chat-based artificial intelligence models for weight loss recommendation appropriateness and bias in the presence of guideline incongruence.
International journal of obesity (2005)
May 1, 2025
BACKGROUND AND AIM: Managing obesity requires a comprehensive approach that involves therapeutic lifestyle changes, medications, or metabolic surgery. Many patients seek health information from online sources and artificial intelligence models like C...
Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning.
PloS one
Jan 1, 2025
Elevated consumption of sugar-sweetened beverages (SSBs) has been associated with an increase in obesity, type 2 diabetes, and other non-communicable diseases (NCDs), a significant health and economic burden on Mongolia. To address this, the governme...
Effect of artificial intelligence driven therapeutic lifestyle changes (AI-TLC) intervention on health behavior and health among obesity pregnant women in China: a randomized controlled trial protocol.
Frontiers in public health
Jan 1, 2025
INTRODUCTION: Obesity has reached epidemic proportions globally, posing significant challenges to public health and economic stability. In China, the prevalence of obesity is increasing rapidly, particularly among pregnant women, who face unique risk...
Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.
PloS one
Jan 1, 2025
Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weight gain is augmenting the disease burden and healthcare expenditure. However, predictors of psychotro...
Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns.
Obesity (Silver Spring, Md.)
Nov 1, 2024
OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct ca...
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