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Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and machine learning offer practical tools for analyzing large health datasets and supporting…
With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to complex real-world clinical data is a persistent problem. Deep…
With the growing use of new technologies, healthcare is nowadays undergoing significant changes. Information-based medicine has to exploit medical decision-support systems and requires the analysis of various, heterogeneous data, such as…
The increasing popularity of machine learning approaches and the rising awareness of data protection and data privacy presents an opportunity to build truly secure and trustworthy healthcare systems. Regulations such as GDPR and HIPAA…
The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have promoted…
Requirements engineering is known to be a key factor for the success of software projects. Inside this discipline, goal-oriented requirements engineering approaches have shown specially suitable to deal with projects where it is necessary…
The development of clinical natural language processing (NLP) systems is severely hampered by the sensitive nature of medical records, which restricts data sharing under stringent privacy regulations, particularly in France and the broader…
Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable sensing technologies, particularly wrist-worn devices, offer promising solutions for…
Scientific workflows process extensive data sets over clusters of independent nodes, which requires a complex stack of infrastructure components, especially a resource manager (RM) for task-to-node assignment, a distributed file system…
Forensic science suffers from a lack of studies with high-quality design, such as randomized controlled trials (RCT). Evidence in forensic science may be of insufficient quality, which is a major concern. Results from RCT are criticized for…
While performing knowledge-intensive tasks of professional nature, the knowledge workers need to access and process large volume of information. Apart from the quantity, they also require that the information received is of high quality in…
This thesis addresses the question of how usable openEHR-aware clinical decision support can be designed and developed in order to improve the quality of health care. To answer this research question, several sub-questions were identified…
This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple,…
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size,…
In the early 21st century, the open data movement began to transform societies and governments by promoting transparency, innovation, and public engagement. The City of New York (NYC) has been at the forefront of this movement since the…
Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the…
Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients'…
High-quality computational and data-intensive (CDI) applications are critical for advancing research frontiers in almost all disciplines. Despite their importance, there is a significant gap due to the lack of comprehensive best practices…
Background: Clinical decision support systems (CDSS) are a category of health information technologies that can assist clinicians to choose optimal treatments. These support systems are based on clinical trials and expert knowledge;…
Effective visualizations were evaluated to reveal relevant health patterns from multi-sensor real-time wearable devices that recorded vital signs from patients admitted to hospital with COVID-19. Furthermore, specific challenges associated…