The advancements in healthcare practice have brought to the fore the need for flexible access to health-related information and created an ever-growing demand for the design and the development of data management infrastructures for translational and personalized medicine. In this paper, we present the data management solution implemented for the MyHealthAvatar EU research project, a project that attempts to create a digital representation of a patient's health status. The platform is capable of aggregating several knowledge sources relevant for the provision of individualized personal services. To this end, state of the art technologies are exploited, such as ontologies to model all available information, semantic integration to enable data and query translation and a variety of linking services to allow connecting to external sources. All original information is stored in a NoSQL database for reasons of efficiency and fault tolerance. Then it is semantically uplifted through a semantic warehouse which enables efficient access to it. All different technologies are combined to create a novel web-based platform allowing seamless user interaction through APIs that support personalized, granular and secure access to the relevant information.
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http://dx.doi.org/10.1109/EMBC.2015.7318630 | DOI Listing |
Pilot Feasibility Stud
January 2025
Advocate Christ Medical Center, Advocate Health, Oak Lawn, IL, USA.
Background: Hypertension is the leading risk factor for cardiovascular disease (CVD). Despite advances in blood pressure management, significant racial and ethnic disparities persist, resulting in higher risks of stroke, heart disease, and mortality among non-White populations. Self-measured blood pressure (SMBP) monitoring, also known as home blood pressure monitoring, has shown promise in improving blood pressure control, especially when combined with feedback from healthcare providers.
View Article and Find Full Text PDFTrop Med Health
January 2025
Department of Community Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Background: Neurobrucellosis, a serious central nervous system infection caused by Brucella species, presents significant challenges due to its diverse clinical manifestations and the risk of long-term complications and poor outcomes. Identifying predictors of adverse outcomes is critical for improving patient management and overall prognosis.
Objectives: This study aimed to evaluate the long-term morbidity and mortality associated with neurobrucellosis and to identify key predictors of adverse outcomes.
Crit Care
January 2025
Department of Neuro-Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background And Objectives: Antibody-negative autoimmune encephalitis (AE) is a form of encephalitis characterized by the absence of detectable autoimmune antibodies, despite immunological evidence. However, data on management of patients with antibody-negative AE in the intensive care unit (ICU) are limited. This study aimed to explore the characteristics and subtypes of antibody-negative AE, assess the effects of immunotherapy, and identify factors independently associated with poor functional outcomes in patients requiring intensive care.
View Article and Find Full Text PDFTrials
January 2025
Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK.
Background: With the population ageing, more victims of community crime are likely to be older adults. The psychological impact of crime on older victims is significant and sustained, but only feasibility trials have been published regarding potential interventions. The integration of public health and care services and cross-agency working is recommended, but there is little information on how this should be undertaken.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Kenya Medical Research Institute- Center for Global Health Research (KEMRI-CGHR), P.O Box 1578-40100, Kisumu, Kenya.
Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.
Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.
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