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metadata
language: en
tags:
  - summarization
  - bart
  - medical question answering
  - medical question understanding
  - consumer health question
  - prompt engineering
  - LLM
license: apache-2.0
datasets:
  - bigbio/meqsum
widget:
  - text: ' 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.'
model-index:
  - name: medqsum-bart-large-xsum-meqsum
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: Dataset for medical question summarization
          type: bigbio/meqsum
          split: valid
        metrics:
          - type: rogue-1
            value: 54.32
            name: Validation ROGUE-1
          - type: rogue-2
            value: 38.08
            name: Validation ROGUE-2
          - type: rogue-l
            value: 51.98
            name: Validation ROGUE-L
          - type: rogue-l-sum
            value: 51.99
            name: Validation ROGUE-L-SUM
library_name: transformers

MedQSum

drawing

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}}