Bayrak, GıyaseddinToprak, M. ŞakirGaniz, Murat CanKodaz, HalifeKoç, Ural2022-10-082022-10-08202297816436828460926-9630https://doi.org/10.3233/SHTI220609https://hdl.handle.net/20.500.13091/2910Norwegian Centre for E-health Research32nd Medical Informatics Europe Conference, MIE 2022 -- 27 May 2022 through 30 May 2022 -- 179490Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.eninfo:eu-repo/semantics/openAccessBrain HemorrhageDeep LearningNLPRadiologyComputational linguisticsComputerized tomographyDeep learningMedical informaticsNatural language processing systemsRadiationBrain hemorrhageCritical caseDeep learningDomain specificFine tuningHaemorrageHemorrhage detectionLanguage modelLearning classifiersRadiology reportsRadiologybrain hemorrhagehumannatural language processingresearchx-ray computed tomographyDeep LearningHumansIntracranial HemorrhagesNatural Language ProcessingResearch ReportTomography, X-Ray ComputedConference Object10.3233/SHTI2206092-s2.0-85131106697