Semantic and Structural Analysis of MIMIC-CXR radiography reports with NLP Methods

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dc.authoridSezer, Emine/0000-0003-4776-6436
dc.authoridUslu, Ege Erberk/0000-0001-9119-8574
dc.authoridGuven, Zekeriya Anil/0000-0002-7025-2815
dc.contributor.authorUslu, Ege Erberk
dc.contributor.authorSezer, Emine
dc.contributor.authorGueven, Zekeriya Anil
dc.date.accessioned2025-03-20T09:50:35Z
dc.date.available2025-03-20T09:50:35Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractArtificial intelligence that aims to imitate human decision-making processes, using human knowledge as a foundation, is a critical research area with various practical applications in different disciplines. In the health domain, machine learning and image processing techniques are increasingly being used to assist in diagnosing diseases. Many healthcare reports, such as epicrisis summaries prepared by clinical experts, contain crucial and valuable information. In addition to information extraction from healthcare reports, applications such as automatic healthcare report generation are among the natural language processing research areas based on this knowledge and experience. The primary goals are to reduce the workload of clinical experts, minimize the likelihood of errors, and save time to speed up the diagnosis process. The MIMIC-CXR dataset is a huge dataset consisting of chest radiographs and reports prepared by radiology experts related to these images. Before developing a natural language processingbased model, preprocessing steps were applied to the dataset, and the results of syntactic and semantic analyses performed on unstructured report datasets are presented. The results show that most examined words and phrases exhibit minimal semantic inference disparities. The generic named entity recognition method demonstrates comparatively lower effectiveness than the ngram technique in extracting word frequencies. However, named entity recognition facilitated the identification of medical entities within the dataset. This study is expected to provide insights for developing language models, particularly for developing a natural language processing model on the MIMIC-CXR dataset.
dc.identifier.doi10.2339/politeknik.1395811
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue5
dc.identifier.scopusqualityN/A
dc.identifier.trdizinid1288795
dc.identifier.urihttps://doi.org/10.2339/politeknik.1395811
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1288795
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2262
dc.identifier.volume27
dc.identifier.wosWOS:001192399700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherGazi Univ
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectNatural language processing
dc.subjectMIMIC-CXR
dc.subjectchest radiology report
dc.subjectstructural analysis
dc.subjectsemantic analysis
dc.titleSemantic and Structural Analysis of MIMIC-CXR radiography reports with NLP Methods
dc.typeArticle

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