Yikuan8

Clinical-Longformer

yikuan8

52

Clinical-Longformer is a variant of the Longformer model that has been further pre-trained on clinical notes from the MIMIC-III dataset. This allows the model to handle longer input sequences of up to 4,096 tokens and achieve improved performance on a variety of clinical NLP tasks compared to the original ClinicalBERT model. The model was initialized from the pre-trained weights of the base Longformer and then trained for an additional 200,000 steps on the MIMIC-III corpus. The maintainer, yikuan8, also provides a similar model called Clinical-BigBIrd that is optimized for long clinical text. Compared to Clinical-Longformer, the Clinical-BigBIrd model uses the BigBird attention mechanism which is more efficient for processing long sequences. Model inputs and outputs Inputs Clinical text data, such as electronic health records or medical notes, with a maximum sequence length of 4,096 tokens. Outputs Depending on the downstream task, the model can be used for a variety of text-to-text applications, including: Named entity recognition (NER) Question answering (QA) Natural language inference (NLI) Text classification Capabilities The Clinical-Longformer model consistently outperformed the ClinicalBERT model by at least 2% on 10 different benchmark datasets covering a range of clinical NLP tasks. This demonstrates the value of further pre-training on domain-specific clinical data to improve performance on healthcare-related applications. What can I use it for? The Clinical-Longformer model can be useful for a variety of healthcare-related NLP tasks, such as extracting medical entities from clinical notes, answering questions about patient histories, or classifying the sentiment or tone of physician communications. Organizations in the medical and pharmaceutical industries could leverage this model to automate or assist with clinical documentation, patient data analysis, and medication management. Things to try One interesting aspect of the Clinical-Longformer model is its ability to handle longer input sequences compared to previous clinical language models. Researchers or developers could experiment with using the model for tasks that require processing of full medical records or lengthy treatment notes, rather than just focused snippets of text. Additionally, the model could be fine-tuned on specific healthcare datasets or tasks to further improve performance on domain-specific applications.

Read more