Federated learning initiatives in healthcare are being developed to collaboratively train
predictive models without the need to centralize sensitive personal data. GenoMed4All is …

There has been much research activity in recent times about providing the data
infrastructures needed for the provision of personalised healthcare. In particular the …

Objectives Distributed computations facilitate multi-institutional data analysis while avoiding
the costs and complexity of data pooling. Existing approaches lack crucial features, such as …

The “Coherent Data Set” is a novel synthetic data set that leverages structured data from
Synthea™ to create a longitudinal,“coherent” patient-level electronic health record (EHR) …

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 …

A disease is an abnormal condition that negatively impacts the functioning of the human
body. Pathology determines the causes behind the disease and identifies its development …

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

The electronic health record (EHR) contains a diverse set of clinical observations that are
captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect …

Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad
range of disciplines in recent years, including health. The increase in computer hardware …

Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …