Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression
It addresses the need to convert unstructured clinical notes into structured data for missing diagnoses, but is incremental as it applies existing NLP techniques to a specific medical domain.
This study tackled the problem of extracting periodontitis diagnoses from clinical notes by combining regular expression methods with a RoBERTa-based NER model, achieving F1 scores up to 0.99 with advanced methods.
This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expression (RE) methods. Two complexity levels of RE methods were used to extract and generate the training data. The SpaCy package and RoBERTa transformer models were used to build the NER model and evaluate its performance with the manual-labeled gold standards. The comparison of the RE methods with the gold standard showed that as the complexity increased in the RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER models demonstrated excellent predictions, with the simple RE method showing 0.84-0.92 in the evaluation metrics, and the advanced and combined RE method demonstrating 0.95-0.99 in the evaluation. This study provided an example of the benefit of combining NER methods and NLP models in extracting target information from free-text to structured data and fulfilling the need for missing diagnoses from unstructured notes.