Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.

Objectives: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG.

Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network.

Aims: We aim to develop a pragmatic screening tool for heart failure at the general population level.

Methods And Results: This study was conducted within the Hamburg-City-Health-Study, an ongoing, prospective, observational study enrolling randomly selected inhabitants of the city of Hamburg aged 45-75 years. Heart failure was diagnosed per current guidelines.

Relationship Between Time of Admission, Help-Seeking Behavior, and Psychiatric Outcomes: "From Dusk Till Dawn".

Introduction: Day and time of admission influence treatment outcomes and prognosis in several medical specialties; this seems related to resources' ability. It is largely unknown whether this also applies to mental health services. We investigate the relationship between time of admission, patients' demographic and clinical profile, and treatment outcomes.

Real-Time Assessment of Stress and Stress Response Using Digital Phenotyping: A Study Protocol.

Stress is a complex phenomenon that may have a negative influence on health and well-being; consequently, it plays a pivotal role in mental health. Although the incidence of mental disorders has been continuously rising, development of prevention and treatment methods has been rather slow. Through the ubiquitous presence of smartphones and wearable devices, people can monitor stress parameters in everyday life.