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Modern healthcare facilities demand digital accessibility to guarantee equal access to telemedicine platforms, online pharmacy services, and health monitoring devices that can be worn or are handy. With the rising call for the…
The translation of clinical decision support system (CDSS) tools from research settings into the clinic is often non-existent, partly because the focus tends to be on training machine learning models rather than tool development using the…
In the context of surveillance of health problems, the research carried out by the French national occupational disease surveillance and prevention network (R\'eseau National de Vigilance et de Pr\'evention des Pathologies Professionnelles,…
Health-related mobile applications are known as eHealth apps. These apps make people more aware of their health, help during critical situations, provide home-based disease management, and monitor/support personalized care through…
In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodel mobile sensing…
Healthcare data is a valuable resource for research, analysis, and decision-making in the medical field. However, healthcare data is often fragmented and distributed across various sources, making it challenging to combine and analyze…
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data…
This paper presents architecture for health care data warehouse specific to cancer diseases which could be used by executive managers, doctors, physicians and other health professionals to support the healthcare process. The data today…
Movement disorders are becoming one of the leading causes of functional disability due to aging populations and extended life expectancy. Wearable health monitoring is emerging as an effective way to augment clinical care for movement…
Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific…
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have…
To ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data…
Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards vary by country and hospital, making records often incompatible. This limits large-scale…
This article presents the potential use of the Self-Sovereign Identities (SSI), combining with Distributed Ledger Technologies (DLT), to improve the privacy and control of health data. The paper presents the SSI technology, lists the…
The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records…
Electronic health records (EHRs) form an invaluable resource for training clinical decision support systems. To leverage the potential of such systems in high-risk applications, we need large, structured tabular datasets on which we can…
The RECONNECT project addresses the fragmentation of Ireland's public healthcare systems, aiming to enhance service planning and delivery for chronic disease management. By integrating complex systems within the Health Service Executive…
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on…
With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is…
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources…