Extraction Of Clinical Entities from Chest Radiology Reports Using NLP Methods

Abstract

Radiology reports are essential for clinical decision-making and diagnosis, containing complex and detailed information. However, their unstructured nature makes efficient processing and analysis challenging, increasing the workload of healthcare professionals and slowing down clinical workflows. Natural Language Processing (NLP) techniques provide effective solutions by extracting meaningful information from such texts, reducing expert workload, and expediting decision-making processes. This study focuses on Named Entity Recognition (NER) in chest radiology reports using the RadGraph dataset, annotated with four tag types. The objective is to compare the performance of two NLP models—BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long Short-Term Memory) —to identify the most suitable approach for clinical data. Various training parameters, including learning rate, optimization algorithm, and input size, were optimized to enhance model performance. To address the class imbalance in the dataset, data augmentation techniques were applied, and both models were fine-tuned. The results revealed that BERT, leveraging its attention mechanism, demonstrated superior performance in identifying complex terms and entities, outperforming LSTM in accuracy, precision, recall, and F1 score. While LSTM effectively captured long-term dependencies, it required longer training times. This research highlights the potential of NLP in automating the extraction of clinical entities from radiology reports. It provides valuable insights for optimizing models and developing clinical decision support systems, ultimately aiming to enhance the efficiency of healthcare workflows.

Keywords

• Deep Learning
• Natural Language Processing
• Named Entity Recognation
• Radiological Report
• BERT

NLP Yöntemleri Kullanılarak Göğüs Radyolojisi Raporlarından Klinik Varlıkların Çıkarılması

Öz

Klinik karar verme ve tanı koyma süreçlerinde büyük önem taşıyan radyoloji raporlarının zengin ancak karmaşık olması, verilerin verimli bir şekilde işlenmesini ve analiz edilmesini zorlaştırmaktadır. Bu durum sağlık çalışanlarının iş yükünü artırmakta ve klinik iş akışlarını yavaşlatmaktadır. Doğal Dil İşleme (NLP) teknikleri, bu tür metinlerden anlamlı bilgileri çıkarıp işleyerek değerli çözümler sunar, böylece uzman iş yükünü azaltır ve karar verme sürecini hızlandırır. Bu çalışmada, dört farklı etiket türüyle etiketlenmiş RadGraph veri kümesini kullanarak göğüs radyolojisi raporlarından Adlandırılmış Varlık Tanıma (NER) üzerine odaklanıyoruz. Amaç, iki modelin performansını karşılaştırmaktır: BERT (Transformatörlerden Çift Yönlü Kodlayıcı Temsilleri) ve LSTM (Uzun Kısa Süreli Bellek). Klinik veriler için en etkili kombinasyonu bulmak amacıyla öğrenme oranı, optimizasyon algoritması ve girdi boyutu gibi çeşitli eğitim parametreleri test edilmiştir. Veri kümesinin etiket dağılımındaki dengesizlik, veri artırımı yoluyla giderilmiş ve her iki modele de ince ayar yapılmıştır. Sonuçlar, BERT'i n dikkat mekanizmasıyla karmaşık terimleri ve varlıkları tanımlamada mükemmel olduğunu ve doğruluk, kesinlik, geri çağırma ve F1 puanı açısından LSTM' den daha iyi performans gösterdiğini göstermiştir. LSTM uzun vadeli bağımlılıkları iyi bir şekilde ele almasına rağmen, daha uzun eğitim sürelerine sahipti. Bu araştırma, NLP' nin tıbbi metinlerden varlık çıkarımını otomatikleştirmedeki etkinliğinin altını çizmekte ve gelecekteki model optimizasyonu ve klinik karar destek sistemi geliştirme için değerli bilgiler sunmaktadır.

Anahtar Kelimeler

• Derin Öğrenme
• Doğal Dil İşleme
• Adlandırılmış varlık tanıma
• Radyolojik Rapor
• BERT

Kaynakça

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