Rich Internet Applications (RIAs) are an emerging software platform that blurs the line between web service and native application, and is a powerful tool for handheld device deployment. By democratizing health data management and widening its availability, this software platform has the potential to revolutionize telemedicine, clinical practice, medical education and information distribution, particularly in rural areas, and to make patient-centric medical computing a reality. In this paper, we propose a telemedicine application that leverages the ability of a mobile RIA platform to transcode, organise and present textual and multimedia data, which are sourced from medical database software. We adopted a web-based approach to communicate, in real-time, with an established hospital information system via a custom RIA. The proposed solution allows communication between handheld devices and a hospital information system for media streaming with support for real-time encryption, on any RIA enabled platform. We demonstrate our prototype's ability to securely and rapidly access, without installation requirements, medical data ranging from simple textual records to multi-slice PET-CT images and maximum intensity (MIP) projections.
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http://dx.doi.org/10.1109/IEMBS.2009.5332734 | DOI Listing |
Diabetes Metab Syndr
December 2024
Department Nursing and Podiatry. Faculty of Health Sciences. University of Málaga, Malaga, Spain. Electronic address:
Introduction: This study explored the effectiveness of current placenta-derived biomaterials therapies in ulcer healing in DFU compared to standard of care (SOC).
Methods: The systematic review and meta-analysis were performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard. The electronic databases of PubMed, EMBASE, and Web of Science (WoS) internet were searched for the outcome rate of complete ulcer healing.
Neural Netw
November 2024
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Intelligent Computation of Public Service Supply, China. Electronic address:
Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining. However, traditional deep neural networks for stance detection often suffer from limitations, including the requirement for large amounts of labeled data, uninterpretability of prediction results, and difficulty in incorporating human intentions and domain knowledge. This paper introduces the First-Order Logic Aggregated Reasoning framework (FOLAR), an innovative approach that integrates first-order logic (FOL) with large language models (LLMs) to enhance the interpretability and efficacy of stance detection.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar Ethiopia.
Data Brief
December 2024
Faculty of Information Science and Technology, Multimedia University Melaka Campus, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia.
This study presents the "ESP32 Dataset," a dataset of radio frequency (RF) data intended for human activity detection. This dataset comprises 10 activities carried out by 8 volunteers in three different indoor floor plan experiment setups. Line-of-sight (LOS) scenarios are represented by the first two experiment setups, and non-line-of-sight (NLOS) scenarios are simulated in the third experiment setup.
View Article and Find Full Text PDFJ Neurosurg
November 2024
1NeurosurGen Inc., Augusta, Georgia.
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