Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient management and quality improvement in healthcare. However, achieving accurate predictions requires the integration of multimodal clinical data, including physiological signals, demographics, and patient history, to estimate prognosis. The integration of such high-dimensional, multi-modal data presents a significant challenge due to its complexity and the need for sophisticated analytical methods.

Knowledge of, attitude towards, and prevalence of tobacco use among dental students in Himachal Pradesh State, India.

Aim: The aim of the study was to assess the prevalence of tobacco use and knowledge of and attitude towards cessation counselling among dental students in Himachal Pradesh State, India.

Method: The present study was conducted with the 219 third-year Bachelor of Dental Surgery (BDS) students in the five dental colleges of Himachal Pradesh during the month of March 2012. The Global Health Professional Students Survey (GHPSS) questionnaire, developed by the World Health Organization (WHO), the US Centers for Disease Control and Prevention (CDC) and the Canadian Public Health Association, was used in this study.