SNOMED Clinical Terms is a comprehensive concept-based health care terminology that was created by merging SNOMED RT and Clinical Terms Version 3. Following the mapping of concepts and descriptions into a merged database, the terminology was further refined by adding new content, modeling the relationships of individual concepts, and reviewing the hierarchical structure. A quality control process was performed to ensure integrity of the data. Additional features such as subsets, qualifiers, and mappings to other coding systems were added or updated to facilitate usability. We then analyzed the content of the completed work. This paper describes the refinement processes and compares the actual content of SNOMED CT with the early data obtained from analysis of the description mapping process. As predicted, the majority of concepts in SNOMED CT originated from SNOMED RT or CTV3, but not both.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244575 | PMC |
Publication Analysis
Top Keywords
Similar Publications
Large language models vs human for classifying clinical documents.
Int J Med Inform
January 2025
College of Science and Engineering, James Cook University, Townsville, 4811, QLD, Australia. Electronic address:
Background: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and Systematized Nomenclature of Medicine (SNOMED) mapping has shown promise in performing these classifications. However, challenges remain, particularly in reducing false negatives, where certain diagnoses are not correctly identified by either approach.
View Article and Find Full Text PDFInternational Expert-Based Consensus Definition, Classification Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study.
Int J Radiat Oncol Biol Phys
January 2025
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Purpose: Osteoradionecrosis of the jaw (ORNJ) is a severe iatrogenic disease characterized by bone death after radiation therapy (RT) to the head and neck. With over 9 published definitions and at least 16 classification systems, the true incidence and severity of ORNJ are obscured by lack of a standard for disease definition and severity assessment, leading to inaccurate estimation of incidence, reporting ambiguity, and likely under-diagnosis worldwide. This study aimed to achieve consensus on an explicit definition and phenotype of ORNJ and related precursor states through data standardization to facilitate effective diagnosis, monitoring, and multidisciplinary management of ORNJ.
View Article and Find Full Text PDFSNOMED-CT and learning health systems for NHS dentistry - a dream needing to become reality.
Br Dent J
January 2025
Professor of Oral and Maxillofacial Surgery, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK; Bloomsbury Trust, London, UK.
There has been discussion and confusion about SNOMED-CT (systematised nomenclature of medicine clinical terminology), learning health systems (LHSs) and their relevance in dentistry. This article aims to provide an overview of SNOMED-CT, LHSs and the all-too-often omitted patient and service benefits from their use. LHSs are delivering impactful benefits to patients and services globally in medicine.
View Article and Find Full Text PDFLeveraging Transformers-based models and linked data for deep phenotyping in radiology.
Comput Methods Programs Biomed
January 2025
Laberit, Avda. de Catalunya, 9, València, 46020, Spain.
Background And Objective: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.
View Article and Find Full Text PDFA pipeline for harmonising NHS Scotland laboratory data to enable national-level analyses.
J Biomed Inform
January 2025
Health Informatics Centre, School of Medicine, University of Dundee, UK; Health Data Research UK, London, UK. Electronic address:
Objective: Medical laboratory data together with prescribing and hospitalisation records are three of the most used electronic health records (EHRs) for data-driven health research. In Scotland, hospitalisation, prescribing and the death register data are available nationally whereas laboratory data is captured, stored and reported from local health board systems with significant heterogeneity. For researchers or other users of this regionally curated data, working on laboratory datasets across regional cohorts requires effort and time.
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!