A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days.

Background: Current atherosclerotic cardiovascular disease (ASCVD) risk assessment tools like the Pooled Cohort Equations (PCEs) and PREVENT™ scores offer long-term predictions but may not effectively drive behavior change. Short-term risk predictions using mobile health (mHealth) data and electronic health records (EHRs) could enhance clinical decision-making and patient engagement. The aim of this study was to develop a short-term ASCVD risk prediction model for hypertensive individuals using mHealth and EHR data and compare its performance to existing risk assessment tools.

[THE TIME HAS COME TO CHANGE THE FACE OF MEDICINE: THE TRANSITION FROM A SINGLE PROFESSIONAL TO A MULTIDISCIPLINARY TEAM TO IMPROVE THE QUALITY OF CARE AND WITHSTAND THE PERSONAL AND PROFESSIONAL BURNOUT OF THE PHYSICIAN].

The Israeli health system is experiencing an ongoing manpower crisis that will deepen soon with the increase in the number of patients and the overcrowding in clinics and hospitals. The core of the crisis is the need to staff the hospitalization departments and clinics with quality manpower. Alongside the initial staffing, there is an obligation to ensure the survivability of the professional personnel in the system over the years, and this must be done while constantly preventing the process of professional burnout.

Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections.

Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data.

Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management.

Background: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints.

Advancing the future of equitable access to health care: recommendations from international health care leaders.

Disparities in access to health care are persistent and contribute to poor health outcomes for many populations around the world. Barriers to access are often similar across countries, despite differences in how health systems are structured. Health care leaders can work to address these barriers through bold, evidence-based actions.