The author explains the term computer network in health institutions, incl. links with the hospital information system. He describes basic technical principles necessary for the understanding of medical network applications in the framework of the information system. The author mentions the importance of connecting the hospital network to international computer networks.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFColor fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features.
Front Cell Dev Biol
January 2025
Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
Introduction: Diabetic retinopathy (DR) has long been recognized as a common complication of diabetes, making accurate automated grading of its severity essential. Color fundus photographs play a crucial role in the grading of DR. With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.
View Article and Find Full Text PDFMambaTab: A Plug-and-Play Model for Learning Tabular Data.
Proc (IEEE Conf Multimed Inf Process Retr)
August 2024
Department of Computer Science, University of Kentucky, Lexington, KY, USA.
Despite the prevalence of images and texts in machine learning, tabular data remains widely used across various domains. Existing deep learning models, such as convolutional neural networks and transformers, perform well however demand extensive preprocessing and tuning limiting accessibility and scalability. This work introduces an innovative approach based on a structured state-space model (SSM), MambaTab, for tabular data.
View Article and Find Full Text PDFEquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.
Comput Struct Biotechnol J
December 2024
Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA.
Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model.
View Article and Find Full Text PDFSynthetic data in generalizable, learning-based neuroimaging.
Imaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!