---
annotations_creators:
- machine-generated
language:
- en
license: cc-by-4.0
tags:
- medical
- rag
- synthetic-qa
- lay-symptom
pretty_name: MediMaven-QA v1.0
size_categories:
- 100K<n<1M
dataset_info:
  config_name: qa_long
  features:
  - name: chunk_id
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: train
    num_bytes: 52621793
    num_examples: 143280
  download_size: 26138154
  dataset_size: 52621793
configs:
- config_name: qa_long
  data_files:
  - split: train
    path: qa_long/train-*
---

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# 🩺 MediMaven-QA v1.0  

**MediMaven-QA** is a *chunk-level, citation-preserving* medical question-answer corpus purpose-built for **Retrieval-Augmented Generation (RAG)**.  
It bridges everyday **lay-symptom narratives** with trustworthy **clinical content** from curated web sources.

## 📦 Dataset Contents

| Config&nbsp;(`name`) | Rows | What it holds | Typical use-case |
|----------------------|------:|---------------|------------------|
| `chunks`   | 70 248 | 200-token, sentence-aware context windows with rich metadata (`id`, `url`, `title`, `section`, `source`, `n_token`, `text`) | RAG context store / retriever training |
| `qa_wide` | 70 018 | *List-of-dict* QA per `chunk_id` <br>→ single row may have ≥1 QA pair | Fast retrieval + generation, keeps chunk linkage |
| `qa_long` | 143 221 | Fully exploded (`chunk_id`, `question`, `answer`) | Classic supervised QA fine-tuning or eval |

> ⚠️ **Disclaimer** — This corpus is for *research & benchmarking only*.  
> It is **not** a diagnostic tool and should not be used in clinical workflows.

## 🚀 Quick Load

```python
from datasets import load_dataset

# pick one of these configs
qa_long  = load_dataset("bernard-kyei/medimaven-qa-data", "qa_long", split="train")
qa_long  = load_dataset("bernard-kyei/medimaven-qa-data", "qa_long", split="train")
# accompany with chunks to get contexts
chunks   = load_dataset("bernard-kyei/medimaven-qa-data", "kb_chunks",  split="train")

print(qa_long[0]["question"])
print(qa_long[0]["answer"])
```

# 🛠️ Generation Pipeline

| Stage               | Tooling                                     | Notes                               |
|---------------------|---------------------------------------------|-------------------------------------|
| 1️⃣ **Crawl**        | Scrapy + Splash                             | Mayo Clinic, NHS.uk, WebMD, Cleveland Clinic (public-domain / permissive T\&Cs) |
| 2️⃣ **Chunk**        | spaCy sentenciser                           | ≈200 tokens / chunk; keeps heading context  |
| 3️⃣ **Synthetic QA** | GPT-4o-mini (`gpt-4o-mini-2024-05-preview`) | • 1 concise lay Q <br>• 1 symptom-narrative Q <br>→ cost **\$40** for 143 k pairs |
| 4️⃣ **Versioning**   | Weights & Biases Artifacts                  | `kb_chunks`, `qa_wide` `qa_long`  |


# 📊 Key Stats
| Metric                  |      Value |
| ----------------------- | ---------: |
| Total context tokens    | **27.4 M** |
| Avg. tokens / chunk     |        390 |
| Unique host domains     |          4 |
| QA pairs / chunk (mean) |        2.0 |
| % symptom-narrative Qs  |       51 % |

# 🧩 Dataset Structure (Arrow schema)
<details><summary>click to expand</summary>
┌─────────────┬──────────────────────┐
│ chunks      │ qa_wide / qa_long    │
├─────────────┼──────────────────────┤
│ id: string  │ chunk_id: string     │
│ url: string │ question: string     │
│ title: str  │ answer: string       │
│ section:str │ -- qa_wide only --   │
│ source:str  │ qa: list<question…>  │
│ text: str   │                     │
│ n_token:int │                     │
└─────────────┴──────────────────────┘
</details>

# 📜 Citation
```bibtex
@misc{KyeiMensah2025MediMavenQA,
  author  = {Kyei-Mensah, Bernard},
  title   = {MediMaven-QA: A Citation-Preserving Medical Q\A Dataset with Symptom Narratives},
  year    = {2025},
  url     = {https://huggingface.co/datasets/dranreb1660/medimaven-qa-data},
  note    = {Version 1.0}
}
```

# 🗒️ Changelog
| Date (UTC)     | Version | Highlights                                                                               |
| -------------- | ------- | ---------------------------------------------------------------------------------------- |
| **2025-05-27** | `v1.0`  | • Sentence-aware chunking <br>• 143 k synthetic QA pairs <br>• Cost optimisation to \$25 |