README.md

---
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
---
[![](https://img.shields.io/badge/GitHub-Repo-blue)](https://github.com/zekaouinoureddine/MedQSum)

## 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](https://doi.org/10.1109/SITA60746.2023.10373720), 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
```json
{
    "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
```python
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}}
```