Latest AI and machine learning research in obesity for healthcare professionals.
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.
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 ChatGPT, Google Gemini, and Microsoft Copilot before consulting health professionals. This study aims to evaluate the appropriateness of the responses ...
It is more attractive to develop effective strategies to reduce sugar intake without compromising food quality with the rising prevalence of obesity a...
BACKGROUND: As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (F-FDG) positron emission tomography (PET) imag...
Insulin resistance, a precursor to type 2 diabetes, is characterized by impaired insulin action in tissues. Current methods for measuring insulin re...
Length of hospital stay is a critical metric for assessing healthcare quality and optimizing hospital resource management. This study aims to identi...
Birth weight (BW) is a key indicator of neonatal health, with low birth weight (LBW) linked to increased mortality and morbidity. Early prediction o...
A scalable and reliable system is required to analyze the National Health and Nutrition Examination Survey (NHANES) data efficiently to understand h...
Cardiac magnetic resonance imaging is the gold standard for non-invasive cardiac assessment, offering rich spatio-temporal views of the cardiac anat...
Overweight and obesity have emerged as widespread societal challenges, frequently linked to unhealthy eating patterns. A promising approach to enhan...
Neuro-developmental disorders are manifested as dysfunctions in cognition, communication, behaviour and adaptability, and deep learning-based comput...
We propose and create an incentive based recommendation algorithm aimed at improving the lifestyle of diabetic patients. This algorithm is integrate...
Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reaso...
The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learni...
Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths and the third most prevalent malignant tumour worldwide. Early de...
Mild cognitive impairment (MCI) may affect up to 20% of people over 65. Global incidence of MCI is increasing, and technology is being explored for ...
This study investigated healthcare utilization patterns prior to prostate cancer diagnoses, aiming to develop machine learning models for early predic...
Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and inva...
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data f...
The population of older adults is steadily increasing, with a strong preference for aging-in-place rather than moving to care facilities. Consequent...