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The
Publicly Available Clinical BERT Embeddings
paper contains four unique clinicalBERT models: initialized with BERT-Base (
cased_L-12_H-768_A-12
) or BioBERT (
BioBERT-Base v1.0 + PubMed 200K + PMC 270K
) & trained on either all MIMIC notes or only discharge summaries.
This model card describes the Bio+Clinical BERT model, which was initialized from
BioBERT
& trained on all MIMIC notes.
Pretraining Data
The
Bio_ClinicalBERT
model was trained on all notes from
MIMIC III
, a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see
here
. All notes from the
NOTEEVENTS
table were included (~880M words).
Model Pretraining
Note Preprocessing
Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (
en core sci md
tokenizer).
Pretraining Procedures
The model was trained using code from
Google's BERT repository
on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (
BioBERT-Base v1.0 + PubMed 200K + PMC 270K
).
Pretraining Hyperparameters
We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15
and max predictions per sequence = 20).
How to use the model
Load the model via the transformers library:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
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emilyalsentzer Bio_ClinicalBERT online free url in huggingface.co:
Bio_ClinicalBERT is an open source model from GitHub that offers a free installation service, and any user can find Bio_ClinicalBERT on GitHub to install. At the same time, huggingface.co provides the effect of Bio_ClinicalBERT install, users can directly use Bio_ClinicalBERT installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
