BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis

• Published in: Advances in Intelligent Data Analysis XIX Vol. 12695; pp. 263 - 274
• Main Authors: Dai, Zhihao, Li, Zhong, Han, Lianghao
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Electronic medical records; Machine learning; Natural language processing; Semisupervised learning
• ISBN: 3030742504; 9783030742508
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-74251-5_21

Radiologists make the diagnoses of bone fractures through examining X-ray radiographs and document them in radiology reports. Applying information extraction techniques on such radiology reports to retrieve the information of bone fracture diagnosis could yield a source of structured data for medical cohort studies, image labelling and decision support concerning bone fractures. In this study, we proposed an information extraction system of Bone X-ray radiology reports to retrieve the details of bone fracture detection and diagnosis, based on a bio-medically pre-trained Bidirectional Encoder Representations from Transformers (BERT) natural language processing (NLP) model by Google. The model, named as BoneBert, was first trained on annotations automatically generated by a handcrafted rule-based labelling system using a dataset of 6,048 X-ray radiology reports and then fine-tuned on a small set of 4,890 expert annotations. Thus, the model was trained in a “semi-supervised” fashion. We evaluated the performance of the proposed model and compared it with the conventional rule-based labelling system on two typical tasks: Assertion Classification (AC) for bone fracture status detection (positive, negative or uncertainty) and Named Entity Recognition (NER) related to the fracture type, the bone type and location of a fracture occurs. BoneBert outperformed the rule-based system in both tasks, showing great potential for automated information extraction of the detection and diagnosis of bone fracture from radiology reports, such as, the clinical status, type and location of bone fracture, and more related observations.