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A disease-specific language representation model for cerebrovascular disease research
Ching-Heng Lin1, Kai-Cheng Hsu2, Chih-Kuang Liang3
1Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Bioinformatics Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
A new model, StrokeBERT, enhances cerebrovascular disease research by improving the accuracy of analyzing clinical notes. This disease-specific BERT model shows superior performance in tasks like artery stenosis detection and stroke risk prediction.
Area of Science:
- Natural Language Processing
- Medical Informatics
- Biomedical Research
Background:
- Unstructured clinical notes pose challenges for medical research.
- BERT-based models like BioBERT and ClinicalBERT show promise in biomedical language tasks.
Purpose of the Study:
- To evaluate a BERT model specifically pre-trained on cerebrovascular disease data.
- To determine if this specialized model, StrokeBERT, improves research in this area.
Main Methods:
- StrokeBERT was developed by further pre-training BioBERT on a large corpus of cerebrovascular disease clinical texts.
- The model was validated on two tasks: artery stenosis detection and recurrent ischemic stroke risk prediction.
Main Results:
- StrokeBERT achieved improved performance in detecting artery stenosis (AUC 0.968 ± 0.021) compared to ClinicalBERT.
- It also demonstrated better prediction of recurrent ischemic stroke risk (AUC 0.838 ± 0.017).
- StrokeBERT's attention mechanism better identified cerebrovascular disease-related terms.
Conclusions:
- A disease-specific BERT model, like StrokeBERT, enhances performance and accuracy in clinical language processing tasks.
- This specialized model can advance cerebrovascular disease research and has potential for clinical applications.
