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    NLP-Powered Healthcare Insights: A Comparative Analysis for Multi-Labeling Classification With MIMIC-CXR Dataset
    (Institute of Electrical and Electronics Engineers Inc., 2024) Erberk Uslu, Ege; Sezer, Emine; Anil Guven, Zekeriya
    The digitization of the healthcare industry has led to a growing number of applications that use machine learning and image processing techniques to improve the diagnostic process. These applications utilize a variety of medical data, including laboratory results, clinical findings, MRI scans, tomographic images, and radiological images. In addition, free-text healthcare documentation, such as well-structured discharge summaries, contains valuable information. Natural Language Processing encompasses the development of automated systems for generating health reports. This process involves using domain-specific knowledge and prior knowledge to extract relevant information from medical records. This article investigates the use of natural language processing techniques for chest X-ray classification. A total of 14 distinct impressions derived from chest radiography findings from the MIMIC-CXR dataset were used in a multi-label classification procedure. Six distinct language models derived from the BERT language model, along with three distinct classification algorithms, were employed to evaluate the effectiveness of the models and the dataset for multi-label categorization. The experimental results showed a successful prediction rate of 80.47% for 14 distinct impressions within the dataset. © 2024 The Authors.
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    Öğe
    Semantic and Structural Analysis of MIMIC-CXR radiography reports with NLP Methods
    (Gazi Univ, 2024) Uslu, Ege Erberk; Sezer, Emine; Gueven, Zekeriya Anil
    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. 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.