This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a health risk score that outperforms body mass index (BMI)-the most commonly used biomarker for obesity....
BACKGROUND: Although transcatheter aortic valve replacement has emerged as an alternative to surgical aortic valve replacement, it requires extensive healthcare resources, and optimal length of hospital stay has become increasingly important. This st...
International journal of medical informatics
Oct 5, 2024
BACKGROUND: Globally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected data has the advantage of being widely applicable, particularly in low-resource settings. Early int...
AIMS: This study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in patients with type 2 diabetes mellitus and to develop a user-friendly mortality risk prediction tool...
RATIONALE AND OBJECTIVES: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Pat...
Journal of thrombosis and thrombolysis
Oct 3, 2024
Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physi...
Archives of gerontology and geriatrics
Oct 2, 2024
OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using mac...
BACKGROUND: Decision tree algorithms, obtained by machine learning, provide clusters of patients with similar clinical patterns by the identification of variables that best merge with a given dependent variable.
The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained...
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. T...