Latest AI and machine learning research in obesity for healthcare professionals.
Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underlying biological pathways remain challenging to decipher, presumably due to the non-linear associations across multiple mechanisms. In this study, we introduced a multipartite projection method based on mutual information correlations to construct mult...
We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visual analysis, we collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis ...
Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbi...
UNLABELLED: Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across mul...
Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's...
Despite the widespread development of ontologies in many domains of healthcare, the field of colorectal cancer (CRC) presents a notable gap considerin...
The prevalence of obstructive sleep apnea comorbid with diabetes is high while the awareness of diabetes is low. There is a strong need for new diagno...
Cardiovascular disease (CVD) is the leading cause of global mortality, diagnosed primarily through costly imaging modalities which are often overused ...
Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performanc...
Cardiovascular Diseases (CVDs) present a substantial global health burden, with tobacco use as a major risk factor. While extensive research has ident...
Reproductive health issues, including unsafe pregnancy termination, remain a significant concern for women in developing nations. This study focused o...
While physical activity is critical to human health, most people do not meet recommended guidelines. More walkable built environments have the poten...
Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational v...
Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acq...
Nutcracker phenomenon is the compression of the left renal vein between the superior mesenteric artery (SMA) and the abdominal aorta. Nutcracker synd...
Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large v...
To achieve automatic segmentation, quantification, and grading of different regions of leopard spots fundus (FT) using deep learning technology. The ...
Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical mea...
Coronary artery disease (CAD) is a complex, multifactorial, and serious condition influenced by genetic, environmental, and lifestyle factors. Thus, i...
Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of ...