:book: BERT Long Document Classification :book:

an easy-to-use interface to fully trained BERT based models for multi-class and multi-label long document classification.

pre-trained models are currently available for two clinical note (EHR) phenotyping tasks: smoker identification and obesity detection.

To sustain future development and improvements, we interface pytorch-transformers for all language model components of our architectures. Additionally, their is a blog post describing the idea behind the architecture.

This repository contains an updated implementation that corrects an error found in the original version of the preprint

Installation

Install with pip:

pip install bert_document_classification

or directly:

pip install git+https://github.com/AndriyMulyar/bert_document_classification

Use

Maps text documents of arbitrary length to binary vectors indicating labels.

from bert_document_classification.models import SmokerPhenotypingBert
from bert_document_classification.models import ObesityPhenotypingBert

smoking_classifier = SmokerPhenotypingBert(device='cuda', batch_size=10) #defaults to GPU prediction

obesity_classifier = ObesityPhenotypingBert(device='cpu', batch_size=10) #or CPU if you would like.

smoking_classifier.predict(["I'm a document! Make me long and the model can still perform well!"])

More examples.

Replication

Go to the directory /examples/ml4health_2019_replication. This README will give instructions on how to appropriately insert data from DBMI to replicate the results in the paper.

Notes

• For training you will need a GPU.
• For bulk inference where speed is not of concern lots of available memory and CPU cores will likely work.
• Model downloads are cached in ~/.cache/torch/bert_document_classification/. Try clearing this folder if you have issues.

Acknowledgement

If you found this project useful, consider citing our extended abstract.

@misc{mulyar2019phenotyping,
    title={Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models},
    author={Andriy Mulyar and Elliot Schumacher and Masoud Rouhizadeh and Mark Dredze},
    year={2019},
    eprint={1910.13664},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Implementation, development and training in this project were supported by funding from the Mark Dredze Lab at Johns Hopkins University.