clinicalBERT

clinicalBERT

Repository for Publicly Available Clinical BERT Embeddings (NAACL Clinical NLP Workshop 2019)

Using Clinical BERT

UPDATE: You can now use ClinicalBERT directly through the transformers library. Check out the Bio+Clinical BERT and Bio+Discharge Summary BERT model pages for instructions on how to use the models within the Transformers library.

Download Clinical BERT

The Clinical BERT models can also be downloaded here, or via

wget -O pretrained_bert_tf.tar.gz https://www.dropbox.com/s/8armk04fu16algz/pretrained_bert_tf.tar.gz?dl=1

biobert_pretrain_output_all_notes_150000 corresponds to Bio+Clinical BERT, and biobert_pretrain_output_disch_100000 corresponds to Bio+Discharge Summary BERT. Both models are finetuned from BioBERT. We specifically use the BioBERT-Base v1.0 (+ PubMed 200K + PMC 270K) version of BioBERT.

bert_pretrain_output_all_notes_150000 corresponds to Clinical BERT, and bert_pretrain_output_disch_100000 corresponds to Discharge Summary BERT. Both models are finetuned from the cased version of BERT, specifically cased_L-12_H-768_A-12.

Reproduce Clinical BERT

Pretraining

To reproduce the steps necessary to finetune BERT or BioBERT on MIMIC data, follow the following steps:

1. Run format_mimic_for_BERT.py - Note you'll need to change the file paths at the top of the file.
2. Run create_pretrain_data.sh
3. Run finetune_lm_tf.sh

Note: See issue #4 for ways to improve section splitting code.

Downstream Tasks

To see an example of how to use clinical BERT for the Med NLI tasks, go to the run_classifier.sh script in the downstream_tasks folder. To see an example for NER tasks, go to the run_i2b2.sh script.

Contact

Please post a Github issue or contact [email protected] if you have any questions.

Citation

Please acknowledge the following work in papers or derivative software:

Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 72-78, Minneapolis, Minnesota, USA. Association for Computational Linguistics.

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