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license: mit |
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# PerMedCQA: Persian Medical Consumer QA Benchmark |
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**PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian** |
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PerMedCQA is the first large-scale, real-world benchmark for Persian-language medical consumer question answering. It contains anonymized medical inquiries from Persian-speaking users paired with professional responses, enabling rigorous evaluation of large language models in low-resource, health-related domains. |
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--- |
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## 📊 Dataset Overview |
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- **Total entries**: 68,138 QA pairs |
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- **Source platforms**: DrYab, HiSalamat, GetZoop, Mavara-e-Teb |
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- **Timeframe**: Nov 10, 2022 – Apr 2, 2024 |
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- **Languages**: Persian only |
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- **Licensing**: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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- |
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- ## 🔗 Paper |
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- 📄 [Paper on arXiv](https://arxiv.org/abs/2505.18331) |
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- 📊 [Paper with Code page](https://paperswithcode.com/paper/permedcqa-benchmarking-large-language-models) |
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--- |
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## 🧬 Metadata & Features |
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Each example in the dataset includes: |
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- `instance_id`: Unique ID for each QA pair |
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- `Title`: Short user-submitted title |
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- `Question`: Full Persian-language consumer medical question |
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- `Expert_Answer`: Doctor’s response |
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- `Category`: Medical topic (e.g., “پوست و مو”) |
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- `Specialty`: Expert’s medical field (e.g., “متخصص پوست و مو”) |
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- `Age`: Reported patient age |
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- `Weight`: Reported weight (optional) |
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- `Sex`: Patient gender (`"man"` or `"woman"`) |
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- `dataset_source`: Name of the platform (e.g., DrYab, Getzoop) |
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- `Tag`: ICD‑11 label and rationale |
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- `QuestionType`: Question classification tag (e.g., "Contraindication", "Indication") and reasoning |
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--- |
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## 📁 Dataset Structure |
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```json |
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{ |
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"Title": "قرمزی پوست نوزاد بعد از استفاده از پماد", |
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"Category": "پوست و مو", |
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"Specialty": "متخصص پوست و مو", |
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"Age": "1", |
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"Weight": "10", |
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"Sex": "man", |
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"dataset_source": "HiSalamat", |
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"instance_id": 32405, |
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"Tag": { |
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"Tag": 23, |
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"Tag_Reasoning": "The question addresses a skin reaction in an infant following the application of a cream, indicating a dermatological condition." |
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}, |
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"Question": "سلام خسته نباشید. من واسه پسر ۱ سالهام که جای واکسنش سفت شده بود، پماد موضعی استفاده کردم. ولی الان پوستش خیلی قرمز شده و خارش داره. ممکنه حساسیت داده باشه؟ باید چکار کنم؟", |
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"Expert_Answer": "احتمالا پوست نوزاد به ترکیبات پماد حساسیت نشان داده است. مصرف آن را قطع کنید و در صورت ادامه علائم به متخصص پوست مراجعه کنید.", |
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"QuestionType": { |
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"Explanation": "The user asks about an adverse skin reaction following the use of a topical medication on an infant, which is a case of possible side effects.", |
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"QuestionType_Tag": "SideEffect" |
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} |
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} |
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``` |
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--- |
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## 📥 How to Load |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("NaghmehAI/PerMedCQA", split="train") |
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print(ds[0]) |
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``` |
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--- |
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## 🚀 Intended Uses |
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- 🧠 **Evaluation** of multilingual or Persian-specific LLMs in real-world, informal medical domains |
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- 🛠️ **Few-shot or zero-shot** fine-tuning, instruction-tuning |
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- 🌍 **Cultural insights**: Persian language behavior in health-related discourse |
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- ⚠️ **NOT for clinical use**: Informational and research purposes only |
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--- |
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## ⚙️ Data Processing Pipeline |
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### Stage 1: Column Transformation (`change_columns.py`) |
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- Reads CSV/JSON input and transforms them into structured JSON format |
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- Handles single-turn, multi-turn, and multi-expert Q&A data |
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- Cleans and formats the text, removing unnecessary whitespace and newlines |
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- Creates a chat-style JSON file with `user` and `assistant` roles |
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### Stage 2: QA Preprocessing (`preprocess_for_qa.py`) |
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- Truncates multi-turn dialogues to first Q&A pair |
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- Removes: |
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- Empty or invalid messages |
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- Q&A pairs shorter than 3 words |
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- Duplicate Q&A instances |
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- Adds: |
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- `dataset_source` and `instance_id` to each item |
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- Merges cleaned records into `All_QA_preprocessed.json` |
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### Dataset Cleaning Results: |
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| Dataset | Step 1 Removed | Step 2 Removed | Step 3 Removed | Final Records | |
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|--------------|----------------|----------------|----------------|----------------| |
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| Dr_Yab | 63 | 1083 | 25 | 37,905 | |
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| GetZoop | 1005 | 1352 | 8 | 25,502 | |
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| Hi-Salamat | 9580 | 121 | 1 | 5,220 | |
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| Mavara-e-Teb | 0 | 1034 | 92 | 4,789 | |
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--- |
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## 📚 Citation |
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If you use **PerMedCQA**, please cite: |
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```bibtex |
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@misc{jamali2025permedcqa, |
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title={PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language}, |
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author={Jamali, Naghmeh and Mohammadi, Milad and Baledi, Danial and Rezvani, Zahra and Faili, Heshaam}, |
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year={2025}, |
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eprint={2505.18331}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.18331} |
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} |
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``` |
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--- |
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## 📬 Contact |
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For collaboration or questions: |
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📧 Naghmeh Jamali – naghme.jamali.ai@gmail.com, Milad Mohammadi – miladmohammadi@ut.ac.ir, Danial Baledi – baledi.danial@gmail.com. |
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