Bert model fine-tuning for text classification in knee OA radiology reports

Purpose: Traditional Natural Language Processing (NLP) techniques do a good job of understanding relationships between adjacent or nearby words. However, clinical text data such as patient notes or radiology reports require capturing interactions between distant words. BERT (Bidirectional Encoder Representations from Transformers), a new form of deep neural network recently introduced by Google, overcomes this challenge given its ability to comprehend long range word interactions. While BERT has dramatically improved outcomes in NLP tasks in the general domain such as optimizing search results, its performance in domain specific tasks such as analyzing biomedical data is still being explored.