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.

Correction: Longitudinal models for the progression of disease portfolios in a nationwide chronic heart disease population.

[This corrects the article DOI: 10.1371/journal.pone.

Correction: Longitudinal models for the progression of disease portfolios in a nationwide chronic heart disease population.

[This corrects the article DOI: 10.1371/journal.pone.

Longitudinal models for the progression of disease portfolios in a nationwide chronic heart disease population.

Background And Aim: With multimorbidity becoming increasingly prevalent in the ageing population, addressing the epidemiology and development of multimorbidity at a population level is needed. Individuals subject to chronic heart disease are widely multimorbid, and population-wide longitudinal studies on their chronic disease trajectories are few.

Methods: Disease trajectory networks of expected disease portfolio development and chronic condition prevalences were used to map sex and socioeconomic multimorbidity patterns among chronic heart disease patients.