amVAE: Age-aware multimorbidity clustering using variational autoencoders.

Multimorbidity, the co-occurrence of multiple chronic conditions within the same individual, is increasing globally. This is a challenge for the single patients, as these individuals are subject to a heavy disease and treatment burden, yet evidence on the epidemiology and consequences of multimorbidity remains underexplored. Historically, studies aiming to understand multimorbidity patterns predominantly utilized cross-sectional data, neglecting the essential temporal dynamics which shape multimorbidity progression.

Creating a sense of place when implementing a new emergency department in Denmark: a qualitative study.

Background: Physical locations play an essential yet often overlooked role in healthcare implementation processes. Implementation Science frameworks such as the Theoretical Domains Framework, the Consolidated Framework for Implementation Research, and the Implementation in Context framework acknowledge the importance of the physical environment, but they often treat it as a passive backdrop for change. However, from a cultural geographic perspective, spaces and places are dynamic, influencing behavior, social structures, and the acceptance of new practices.

Susceptibility to and severity of SARS-CoV-2 infection according to prescription drug use-an observational study of 46,506 Danish healthcare workers.

It is not well investigated whether exposure to specific drug classes is associated with COVID-19. We investigated the risk of SARS-CoV-2 infection and severe COVID-19 among healthcare workers according to prescription drug use. We conducted an observational study among Danish healthcare workers.

Prospective and External Validation of Machine Learning Models for Short- and Long-Term Mortality in Acutely Admitted Patients Using Blood Tests.

: Predicting mortality in emergency departments (EDs) using machine learning models presents challenges, particularly in balancing simplicity with performance. This study aims to develop models that are both simple and effective for predicting short- and long-term mortality in ED patients. Our approach uses a minimal set of variables derived from one single blood sample obtained at admission.