Background Machine learning and artificial intelligence have shown promising results in
many areas and are driven by the increasing amount of available data. However, these data …

Background: Central collection of distributed medical patient data is problematic due to strict
privacy regulations. Especially in clinical environments, such as clinical time-to-event …

Online application for survival analysis (OASIS) and its update, OASIS 2, have been widely
used for survival analysis in biological and medical sciences. Here, we provide a portable …

The age of big data has fueled expectations for accelerating learning. The availability of
large data sets enables researchers to achieve more powerful statistical analyses and …

Effective disease surveillance systems require large-scale epidemiological data to improve
health outcomes and quality of care for the general population. As data may be limited within …

The digitalization of health care leads to the accumulation of huge amounts of biomedical
data that is used in clinical research and studies to uncover therapies, treatments, or novel …

Bioinformatics is grappling with an explosion of data, creating both opportunities and
challenges for scientific discovery and healthcare. This thesis stands at the crossroads of …

This thesis presents CoLN (Combined Learning Network), a new approach to decentralized
machine learning that employs a non-convex model focused on privacy preservation. Unlike …

Decentralized machine learning focuses on learning from data distributed at multiple related
sites, where due to privacy or regulatory concerns, data pooling is not an option. Examples …

Die Digitalisierung im Gesundheitssektor hat in den letzten Jahren stetig an Bedeutung
gewonnen. Der Band nähert sich diesem Thema aus der rechtlichen Perspektive, die um …