Dutch interface terminologies are needed to use SNOMED CT in the Netherlands. Machine translation may support in their creation. The aim of our study is to compare different machine translations of procedures in SNOMED CT. Procedures were translated using Google Translate, Matecat, and Thot. Google Translate and Matecat are tools with large but general translation memories. The translation memory of Thot was trained and tuned with various configurations of a Dutch translation of parts of SNOMED CT, a medical dictionary and parts of the UMLS Metathesaurus. The configuration with the highest BLEU score, representing closeness to human translation, was selected. Similarity was determined between Thot translations and those by Google and Matecat. The validity of translations was assessed through random samples. Google and Matecat translated similarly in 85.4% of the cases and generally better than Thot. Whereas the quality of translations was considered acceptable, machine translations alone are yet insufficient.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.
J Affect Disord
January 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai, Shandong 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China. Electronic address:
Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).
Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling.
FDA reviewed artificial intelligence-enabled products applicable to emergency medicine.
Am J Emerg Med
December 2024
Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA.
Objective: To identify and assess artificial intelligence (AI)-enabled products reviewed by the U.S. Food and Drug Administration (FDA) that are potentially applicable to emergency medicine (EM).
View Article and Find Full Text PDFAnalyzing the TotalSegmentator for facial feature removal in head CT scans.
Radiography (Lond)
January 2025
Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany.
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.
Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.
AI Methods for Antimicrobial Peptides: Progress and Challenges.
Microb Biotechnol
January 2025
Machine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models.
View Article and Find Full Text PDFA machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma.
Biomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).
Want 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!