For the development of a rheumatology information system, a medical data dictionary was developed that supports all phases of software development. In the design phase, the medical expert described his clinical environment and the rheumatology medical record in a semantic network structure. Causal relationships between different items of the medical record (e.g., a problem may be related to an adverse drug event caused by a particular drug) are also represented in the semantic network and transferred into referential integrity constraints of the patient database. Furthermore, by also integrating the domain management as a feature of the medical data dictionary, the elementary attributes of the medical record and the associated lists of valid attribute entries have also been defined within the semantic network. This structure allowed the automatic generation of data entry screens, thus making the clinical applications as independent from any hardcoded program module as possible.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Table Extraction with Table Data Using VGG-19 Deep Learning Model.
Sensors (Basel)
January 2025
Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.
In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for the precise identification and extraction of rows and columns from document images that contain tables.
View Article and Find Full Text PDFMultiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar.
Sensors (Basel)
January 2025
School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China.
Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks.
View Article and Find Full Text PDFMulti-Scale Contrastive Learning with Hierarchical Knowledge Synergy for Visible-Infrared Person Re-Identification.
Sensors (Basel)
January 2025
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.
Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality retrieval task to match a person across different spectral camera views. Most existing works focus on learning shared feature representations from the final embedding space of advanced networks to alleviate modality differences between visible and infrared images. However, exclusively relying on high-level semantic information from the network's final layers can restrict shared feature representations and overlook the benefits of low-level details.
View Article and Find Full Text PDFData-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer.
Sensors (Basel)
December 2024
Master's Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan.
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex shapes due to their focus on texture features and limited ability to incorporate positional information. As orthopedic surgery increasingly requires precise automatic diagnosis, we explored SegFormer, an enhanced Vision Transformer model that better handles spatial awareness in segmentation tasks.
View Article and Find Full Text PDFLW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11.
Sensors (Basel)
December 2024
College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.
Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images.
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!