Abstract Objectives The HL7® fast healthcare interoperability resources (FHIR®)
specification has emerged as the leading interoperability standard for the exchange of …

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in
biomedicine and particularly timely in precision medicine. Many research groups deliver …

Abstract Background Knowledge graphs (KGs) play a key role to enable explainable
artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs …

Electronic health record (EHR) systems collate patient data, and the integration and
standardization of documents through Health Information Exchange (HIE) play a pivotal role …

Cancer survivorship has traditionally received little research attention although it is
associated with a variety of long-term consequences and also many other comorbidities …

Objective: This study aims to comprehensively review the use of graph neural networks
(GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary …

Background The increasing use of electronic health records and the Internet of Things has
led to interoperability issues at different levels (structural and semantic). Standards are …

Background and objective: Using graph theory, Electronic Health Records (EHRs) can be
represented graphically to exploit the relational dependencies of the multiple information …

Background The prevalence of chronic diseases has shifted the burden of disease from
incidental acute inpatient admissions to long-term coordinated care across healthcare …

Background: Data models are crucial for clinical research as they enable researchers to fully
use the vast amount of clinical data stored in medical systems. Standardized data and well …