ClinGen

This is the code for our paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models.

Model Framework

Dataset

Generated Datasets

The original train/validation/test data, and the generated synthetic training data will be uploaded in Huggingface Dataset Hub soon:

Corpus	# Train	# Test	# Class	Task	Link
LitCovid	24960	6238	7	Text Classification	litcovid
HOC	3091	898	10	Text Classification	hoc
GAD	4750	350	1	Relation Extraction	gad
CDR	8431	2522	1	Relation Extraction	cdr
ChemProt	8793	10807	5	Relation Extraction	chemprot
MedNLI	11232	1422	3	Natural Language Inference	mednli
MEDIQA-NLI	-	405	3	Natural Language Inference	mediqa-nli
MEDIQA-RQE	8588	302	2	Natural Language Inference	mediqa-rqe
PUBHEALTH	9804	1231	4	Fact Verification	pubhealth
HealthVer	10591	1824	3	Fact Verification	healthver
MQP	10	3033	2	Sentence Similarity	mqp
BC5CDR-Disease	4882	5085	1	Named Entity Recognition	bc5cdr-disease
BC5CDR-Chemical	4882	5085	1	Named Entity Recognition	bc5cdr-chemical
NCBI-Disease	5336	921	1	Named Entity Recognition	ncbi-disease
CHEMDNER	14522	12430	1	Named Entity Recognition	chemdner
CASI	5	100	6	Attribute Extraction	casi

Training Data Generation

First of all, please apply an OpenAI API key here, if you don't have one yet. Then, replace the YOUR_API_KEY in clingen.py with your own API key. Finally, run bash run_clingen.sh with your specified dataset name and keyword type.

Questions?

Feel free to contact ran.xu at emory.edu for any questions regarding this repo. Please try to specify the problem with details so we can help you better and quicker!

Citation

If you find this repository helpful, please kindly consider citing the corresponding paper. Thanks in advance!

@misc{xu2023knowledgeinfused,
    title={Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models},
    author={Ran Xu and Hejie Cui and Yue Yu and Xuan Kan and Wenqi Shi and Yuchen Zhuang and Wei Jin and Joyce Ho and Carl Yang},
    year={2023},
    eprint={2311.00287},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}