A collaboration between the International Health Terminology Standards Development Organisation (IHTSDO®) and the World Health Organization (WHO) has resulted in a priority set of cross maps from SNOMED CT® to ICD-10® to support the epidemiological, statistical and administrative reporting needs of the IHTSDO member countries, WHO Collaborating Centres, and other interested parties. Overseen by the Joint Advisory Group (JAG), approximately 20,000 SNOMED CT concepts have been mapped to ICD-10 using a stand-alone mapping tool. The IHTSDO Map Special Interest Group (MapSIG) developed the mapping heuristics and established the validation process in conjunction with the JAG. Mapping team personnel were selected and then required to participate in a training session using the heuristics and tool. Quality metrics were used to assess the training program. An independent validation of cross map content was conducted under the supervision of the American Health Information Management Association. Lessons learned are being incorporated into the plans to complete the mapping of the remaining SNOMED CT concepts to ICD-10.
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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 PDFRofo
December 2024
Department of Radiology, Medical Center - University of Freiburg Department of Radiology, Freiburg, Germany.
In radiology, technological progress has led to an enormous increase in data volumes. To effectively use these data during diagnostics or subsequent clinical evaluations, they have to be aggregated at a central location and be meaningfully retrievable in context. Radiology data warehouses undertake this task: they integrate diverse data sources, enable patient-specific and examination-specific evaluations, and thus offer numerous benefits in patient care, education, and clinical research.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
Barts Cancer Centre, Barts Health NHS Trust, London, UK.
Background: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data.
Methods: Patients aged 40 and above who underwent a chest x-ray (CXR) between 2016 and 2022 were included.
BMC Med Inform Decis Mak
November 2024
CogStack, Guys and St Thomas NHS Trust, London, UK.
Health Inf Sci Syst
December 2024
Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080 China.
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