The clinical element model (CEM) is an information model designed for representing clinical information in electronic health records (EHR) systems across organizations. The current representation of CEMs does not support formal semantic definitions and therefore it is not possible to perform reasoning and consistency checking on derived models. This paper introduces our efforts to represent the CEM specification using the Web Ontology Language (OWL). The CEM-OWL representation connects the CEM content with the Semantic Web environment, which provides authoring, reasoning, and querying tools. This work may also facilitate the harmonization of the CEMs with domain knowledge represented in terminology models as well as other clinical information models such as the openEHR archetype model. We have created the CEM-OWL meta ontology based on the CEM specification. A convertor has been implemented in Java to automatically translate detailed CEMs from XML to OWL. A panel evaluation has been conducted, and the results show that the OWL modeling can faithfully represent the CEM specification and represent patient data.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628064 | PMC |
http://dx.doi.org/10.1136/amiajnl-2012-001326 | DOI Listing |
Noncoding RNA
January 2025
Institute of Pharmaceutical Sciences, ETH Zurich, 8093 Zurich, Switzerland.
Background: Despite tremendous advances in antiretroviral therapy (ART) against HIV-1 infections, no cure or vaccination is available. Therefore, discovering novel therapeutic strategies remains an urgent need. In that sense, miRNAs and miRNA therapeutics have moved intensively into the focus of recent HIV-1-related investigations.
View Article and Find Full Text PDFAJR Am J Roentgenol
January 2025
Department of Radiology, Division of Breast Imaging and Intervention, Mayo Clinic, Phoenix, AZ.
Contrast-enhanced mammography (CEM) is growing in clinical use due to its increased sensitivity and specificity compared to full-field digital mammography (FFDM) and/or digital breast tomosynthesis (DBT), particularly in patients with dense breasts. To perform an intraindividual comparison of MGD between FFDM, DBT, a combination protocol using both FFDM and DBT (combined FFDM-DBT), and CEM, in patients undergoing breast cancer screening. This retrospective study included 389 women (median age, 57.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Bayern, Germany.
Purpose: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.
Methods: AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images.
Aesthetic Plast Surg
January 2025
Department of Plastic and Reconstructive Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1, Shuaifuyuan, Dongcheng District, Beijing, China.
Background: Perioral rejuvenation is challenging due to the lack of spatial anatomical understanding of the labiomandibular fold (LMF). The LMF's formation mechanism remains underexplored due to intricate relationships between musculature and subcutaneous structures. This study aimed to clarify the three-dimensional structures of the LMF using micro-computed tomography and histology.
View Article and Find Full Text PDFNanoscale
January 2025
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Neurodegenerative diseases, characterized by the progressive deterioration of neuronal function and structure, pose significant global public health and economic challenges. Brain-Derived Neurotrophic Factor (BDNF), a key regulator of neuroplasticity and neuronal survival, has emerged as a critical biomarker for various neurodegenerative and psychiatric disorders, including Alzheimer's disease. Traditional diagnostic methods, such as Enzyme-Linked Immunosorbent Assay (ELISA) and electrochemiluminescence (ECL) assays, face limitations in terms of sensitivity, stability, reproducibility, and cost-effectiveness.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!