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dc.contributor.authorLamproudis, Anastasios
dc.contributor.authorOlsen Svenning, Therese
dc.contributor.authorTorsvik, Torbjørn
dc.contributor.authorChomutare, Taridzo Fred
dc.contributor.authorBudrionis, Andrius
dc.contributor.authorNgo, Phuong Dinh
dc.contributor.authorVakili, Thomas
dc.contributor.authorDalianis, Hercules
dc.date.accessioned2024-02-16T14:10:44Z
dc.date.available2024-02-16T14:10:44Z
dc.date.issued2023
dc.description.abstractWith the recent advances in natural language processing and deep learning, the development of tools that can assist medical coders in ICD-10 diagnosis coding and increase their efficiency in coding discharges ummaries is significantly more viable than before. To that end, one important component in the development of these models is the datasets used to train them. In this study, such datasets are presented, and it is shown that one of them can be used to develop a BERT-based language model that can consistently perform well in assigning ICD-10 codes to discharge summaries written in Swedish. Most importantly, it can be used in a coding support setup where a tool can recommend potential codes to the coders. This reduces the range of potential codes to consider and, in turn, reduces the workload of the coder. Moreover, the de-identified and pseudonymised dataset is open to use for academic users.en_US
dc.descriptionSource at <a href=https://knowledge.amia.org/event-data>https://knowledge.amia.org/event-data</a>.en_US
dc.identifier.citationLamproudis, Olsen Svenning, Torsvik, Chomutare, Budrionis, Ngo, Vakili, Dalianis. Using a large open clinical corpus for improved ICD-10 diagnosis coding. AMIA Annual Symposium Proceedings. 2023;2023:465-473en_US
dc.identifier.cristinIDFRIDAID 2212874
dc.identifier.issn1559-4076
dc.identifier.issn1942-597X
dc.identifier.urihttps://hdl.handle.net/10037/32959
dc.language.isoengen_US
dc.relation.journalAMIA Annual Symposium Proceedings
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785868/
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleUsing a large open clinical corpus for improved ICD-10 diagnosis codingen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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