Related papers: DEUS: Distributed Electronic Patient File Update S…
A patient-centric approach to healthcare leads to an informal social network among medical professionals. This chapter presents a research framework to: identify the collaboration structure among physicians that is effective and efficient…
In the current paradigm of digital personalized services, the centralized management of personal data raises significant privacy concerns, security vulnerabilities, and diminished individual autonomy over sensitive information. Despite…
Large-scale radiology data are critical for developing robust medical AI systems. However, sharing such data across hospitals remains heavily constrained by privacy concerns. Existing de-identification research in radiology mainly focus on…
Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is…
There is a clear need to involve patients in medical decisions. However, cognitive psychological research has highlighted the cognitive limitations of humans with respect to 1. Probabilistic assessment of the patient state and of potential…
The ultimate goal in the pharmaceutical sector is product quality. However this quality can be altered by the use of a number of heterogeneous information systems with different business structures and concepts along the lifecycle of the…
Data integration among various stakeholders in the healthcare space remains a challenge, despite the impressive advances in Health AI in the past decade. There is a lot of ``messy'' non-standard but structured data that are continually…
The transformative potential of AI in healthcare - including better diagnostics, treatments, and expanded access - is currently limited by siloed patient data across multiple systems. Federal initiatives are necessary to provide critical…
Medical Dialogue Systems aim to provide automated healthcare support through patient-agent conversations. Previous efforts typically regard patients as ideal users -- one who accurately and consistently reports their health conditions.…
eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature…
Intensive Care Units (ICUs) are critical environments characterized by high-stakes monitoring and complex data management. However, current practices often rely on manual data transcription and fragmented information systems, introducing…
Information Fusion Systems are now widely used in different fusion contexts, like scientific processing, sensor networks, video and image processing. One of the current trends in this area is to cope with distributed systems. In this…
On the Internet large service providers tend to control the digital identities of users. These defacto identity authorities wield significant power over users, compelling them to comply with non-negotiable terms, before access to services…
Falls are a common problem affecting the older adults and a major public health issue. Centers for Disease Control and Prevention, and World Health Organization report that one in three adults over the age of 65 and half of the adults over…
The ideal of a country's health information system (HIS) is to develop processes that ensure easy collection of relevant data and enable their conversion to useful health indicators, which guide decision making and support health…
This study investigates behavioral intention to use healthcare metaverse platforms among medical students and physicians in Turkey, where such technologies are in early stages of adoption. A multi-theoretical research model was developed by…
Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e. platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent…
Decentralized Health Intelligence Network (DHIN) extends the Decentralized Intelligence Network (DIN) framework to address challenges in healthcare data sovereignty and AI utilization. Building upon DIN's core principles, DHIN introduces…
The storage and manipulation of digital images and the analysis of the information held in those images are essential requirements for next-generation medical information systems. The medical community has been exploring collaborative…
This paper introduces a novel hybrid model combining Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) to simulate infectious disease dynamics across geographic regions. By leveraging the spatial detail of…