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

Ege Erberk Uslu; Emine Sezer; Zekeriya Anıl Güven

Makale detayları

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

Yazarlar:

Ege Erberk Uslu | Emine Sezer | Zekeriya Anıl Güven

Yayıncı:

Gazi University

Ana Başlıklar:

Doğal Dil İşleme

Dergi:

Politeknik Dergisi Vol.27 Issue 5, pp.1955-1969

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331587

pISSN:

2147-9429

Anahtar kelimeler:

Özet:

 Artificial 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 using laboratory results, findings, MRI, tomography, or radiology images, and etc. However, 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. This study focuses on the structural and semantic analysis of MIMIC-CXR radiography reports. Before developing a natural language processing-based model, preprocessing steps were applied to the dataset, and the results of syntactic and semantic analyses performed on unstructured report datasets are presented. This study is expected to provide insights for developing language models, particularly for developing a natural language processing model on the MIMIC-CXR dataset.

 Artificial 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 using laboratory results, findings, MRI, tomography, or radiology images, and etc. However, 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. This study focuses on the structural and semantic analysis of MIMIC-CXR radiography reports. Before developing a natural language processing-based model, preprocessing steps were applied to the dataset, and the results of syntactic and semantic analyses performed on unstructured report datasets are presented. This study is expected to provide insights for developing language models, particularly for developing a natural language processing model on the MIMIC-CXR dataset.

 Artificial 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 using laboratory results, findings, MRI, tomography, or radiology images, and etc. However, 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. This study focuses on the structural and semantic analysis of MIMIC-CXR radiography reports. Before developing a natural language processing-based model, preprocessing steps were applied to the dataset, and the results of syntactic and semantic analyses performed on unstructured report datasets are presented. This study is expected to provide insights for developing language models, particularly for developing a natural language processing model on the MIMIC-CXR dataset.

 Yapay zeka, insan karar verme süreçlerini taklit etmeyi ve insan bilgisini temel almayı amaçlayan, farklı disiplinlerde çeşitli pratik uygulama alanına sahip kritik bir araştırma alanıdır. Sağlık alanında, makine öğrenimi ve görüntü işleme teknikleri, laboratuvar sonuçları, bulgular, MRI, tomografi veya radyoloji görüntüleri gibi çeşitli kaynaklardan yararlanarak hastalıkların teşhisine yardımcı olmak için giderek daha fazla kullanılmaktadır. Bununla birlikte, klinik uzmanlar tarafından hazırlanan epikriz özetleri gibi birçok sağlık raporu kritik ve değerli bilgiler içermektedir. Sağlık raporlarından bilgi çıkarmaya ek olarak, otomatik sağlık raporu oluşturma gibi uygulamalar, bilgi ve deneyime dayalı doğal dil işleme araştırma alanlarından biridir. Bu tür uygulamaların temel hedefleri, klinik uzmanların iş yükünü azaltmak, hata olasılığını en aza indirmek ve tanı sürecini hızlandırmak için zamandan tasarruf etmektir. MIMIC-CXR veri seti, radyoloji uzmanları tarafından çekilen göğüs röntgenleri ve bu görüntülerle ilgili raporlardan oluşan bir büyük veri setidir. Bu çalışma, MIMIC-CXR radyoloji raporlarının yapısal ve anlamsal analizine odaklanmaktadır. Doğal dil işleme DDİ tabanlı bir model geliştirmeden önce, veri setine ön işleme adımları uygulanmakta ve yapılandırılmamış rapor veri setlerinde gerçekleştirilen yapısal ve anlamsal analiz sonuçları sunulmaktadır. Bu çalışmanın, dil modellerinin geliştirilmesi, özellikle MIMIC-CXR veri seti üzerinde bir doğal dil işleme modelinin geliştirilmesi için araştırmacılara ışık tutması beklenmektedir.

Makale detayları

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

Yazarlar:

Ege Erberk Uslu | Emine Sezer | Zekeriya Anıl Güven

Yayıncı:

Gazi University

Ana Başlıklar:

Doğal Dil İşleme

Dergi:

Politeknik Dergisi Vol.27 Issue 5, pp.1955-1969

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331587

pISSN:

2147-9429

Natural language processing

MIMIC-CXR

chest radiology report

structural analysis

semantic analysis.Doğal dil işleme

MIMIC-CXR

göğüs radyoloji raporu

yapısal analiz

anlamsal analiz

Sayı

Makale detayları

Yazarlar: Ege Erberk Uslu | Emine Sezer | Zekeriya Anıl Güven

Yayıncı: Gazi University

Ana Başlıklar: Doğal Dil İşleme

Dergi: Politeknik Dergisi Vol.27 Issue 5, pp.1955-1969

Belge Tipi: Makale

Yayın Tarihi: 2/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001331587

pISSN: 2147-9429

Sayı

Yazarlar:

Ege Erberk Uslu | Emine Sezer | Zekeriya Anıl Güven

Yayıncı:

Gazi University

Ana Başlıklar:

Doğal Dil İşleme

Dergi:

Politeknik Dergisi Vol.27 Issue 5, pp.1955-1969

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331587

pISSN:

2147-9429