NouRed/medqsum-bart-large-xsum-meqsum

MedQSum

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

key	value
eval_rouge1	54.32
eval_rouge2	38.08
eval_rougeL	51.98
eval_rougeLsum	51.99

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}}