NouRed/medqsum-bart-large-xsum-meqsum
MedQSum
<a href="https://github.com/zekaouinoureddine/MedQSum"> <img src="https://raw.githubusercontent.com/zekaouinoureddine/MedQSum/master/assets/models.png" alt="drawing" width="600"/> </a>
TL;DR
medqsum-bart-large-xsum-meqsum is the best fine-tuned model in the paper Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach, which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
Hyperparameters
{
"dataset_name": "MeQSum",
"learning_rate": 3e-05,
"model_name_or_path": "facebook/bart-large-xsum",
"num_train_epochs": 4,
"per_device_eval_batch_size": 4,
"per_device_train_batch_size": 4,
"predict_with_generate": true,
}Usage
from transformers import pipeline
summarizer = pipeline("summarization", model="NouRed/medqsum-bart-large-xsum-meqsum")
chq = '''SUBJECT: high inner eye pressure above 21 possible glaucoma
MESSAGE: have seen inner eye pressure increase as I have begin taking
Rizatriptan. I understand the med narrows blood vessels. Can this med.
cause or effect the closed or wide angle issues with the eyelense/glacoma.
'''
summarizer(chq)Results
Cite This
@INPROCEEDINGS{10373720,
author={Zekaoui, Nour Eddine and Yousfi, Siham and Mikram, Mounia and Rhanoui, Maryem},
booktitle={2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)},
title={Enhancing Large Language Models’ Utility for Medical Question-Answering: A Patient Health Question Summarization Approach},
year={2023},
volume={},
number={},
pages={1-8},
doi={10.1109/SITA60746.2023.10373720}}