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README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-generation
+  - question-answering
+language:
+  - en
+tags:
+  - medical
+  - healthcare
+  - ncd
+  - diabetes
+  - hypertension
+  - clinical
+  - ehr
+  - synthetic
+  - instruction-tuning
+  - fine-tuning
+  - gemma
+  - llm
+size_categories:
+  - 10K<n<100K
+pretty_name: Synthea NCD Risk Assessment Instructions
+---
+
+# Synthea NCD Instructions
+
+Synthetic EHR-based instruction-tuning dataset for training LLMs to predict non-communicable disease (NCD) risk, specifically **Type 2 Diabetes** and **Hypertension**.
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("samwell/synthea-ncd-instructions")
+
+# View a sample
+print(dataset["train"][0])
+```
+
+## Dataset Description
+
+This dataset contains instruction-tuning examples derived from synthetic patient records generated using [Synthea](https://github.com/synthetichealth/synthea). Each example presents a patient's clinical data and asks the model to assess their NCD risk.
+
+### Why This Dataset?
+
+- **NCD burden**: Diabetes and hypertension affect billions globally, especially in LMICs
+- **Clinical decision support**: LLMs can help with risk stratification at scale
+- **Open & reproducible**: Fully synthetic, no privacy concerns, Apache 2.0 licensed
+- **Instruction-tuned format**: Ready for fine-tuning Gemma, Llama, Mistral, etc.
+
+### Intended Use
+
+- Fine-tuning LLMs for clinical decision support
+- Training models for NCD risk stratification
+- Research in medical AI and clinical NLP
+- Educational purposes
+- Prototyping healthcare AI applications
+
+### Not Intended For
+
+- Direct clinical use without validation
+- Replacing clinical judgment
+- Diagnosis or treatment decisions
+- Production deployment without clinician review
+
+## Dataset Structure
+
+Each example contains:
+
+| Field | Description |
+|-------|-------------|
+| `instruction` | Task description asking for NCD risk assessment |
+| `input` | Patient record with demographics, vitals, labs, conditions, medications |
+| `output` | Structured risk assessment with risk levels and recommendations |
+
+### Example
+
+```json
+{
+  "instruction": "Based on the following patient record, assess the risk of Type 2 diabetes and hypertension. Provide risk levels (LOW, MODERATE, HIGH, or DIAGNOSED) with supporting factors, and clinical recommendations.",
+  "input": "Patient: 52yo Female\nVitals: BP 142/88 mmHg, BMI 29.4, Weight 78.2 kg\nLabs: Glucose 118 mg/dL, HbA1c 6.0%, Total Cholesterol 210 mg/dL\nActive conditions: Prediabetes\nMedications: None",
+  "output": "## Risk Assessment\n\n**Type 2 Diabetes: MODERATE**\n- Fasting glucose 118 mg/dL (prediabetes range 100-125)\n- HbA1c 6.0% (prediabetes range 5.7-6.4%)\n- BMI 29.4 (overweight)\n- History of prediabetes\n\n**Hypertension: HIGH**\n- BP 142/88 mmHg (Stage 1 hypertension)\n\n## Recommendations\n1. Lifestyle counseling: diet modification, increase physical activity\n2. Recheck glucose/HbA1c in 3-6 months\n3. Confirm elevated BP on 2 separate occasions\n4. Consider initiating antihypertensive therapy"
+}
+```
+
+### Risk Levels
+
+| Level | Description |
+|-------|-------------|
+| **LOW** | No significant risk factors identified |
+| **MODERATE** | Some risk factors present, lifestyle modification recommended |
+| **HIGH** | Multiple risk factors or abnormal values, further workup needed |
+| **DIAGNOSED** | Patient has confirmed diagnosis in their record |
+
+## Data Splits
+
+| Split | Examples | Purpose |
+|-------|----------|---------|
+| train | ~40,000 | Model training |
+| val | ~5,000 | Hyperparameter tuning |
+| test | ~5,000 | Final evaluation |
+
+## Clinical Parameters
+
+### Observations Used
+
+**Vitals:**
+- Blood Pressure (systolic/diastolic)
+- BMI
+- Body Weight
+- Heart Rate
+
+**Labs:**
+- Fasting Glucose
+- HbA1c
+- Total Cholesterol, HDL, LDL, Triglycerides
+- Creatinine, eGFR
+
+### Conditions Tracked
+
+| Condition | SNOMED Code |
+|-----------|-------------|
+| Prediabetes | 714628002 |
+| Type 2 Diabetes | 44054006 |
+| Essential Hypertension | 59621000 |
+| Diabetic Neuropathy | 368581000119106 |
+| Diabetic Retinopathy | 1551000119108 |
+| Diabetic Kidney Disease | 127013003 |
+
+## Generation Process
+
+1. **Synthea** generated synthetic patient populations with realistic disease progression
+2. Patient records were filtered for those with relevant NCD observations
+3. Risk assessments were generated using clinical guidelines:
+   - ADA criteria for diabetes/prediabetes
+   - ACC/AHA guidelines for hypertension
+4. Data was formatted for instruction-tuning (Alpaca-style)
+
+### Reproducibility
+
+The generation scripts are available at: [github.com/HopeOS/training](https://github.com/hopeos/training)
+
+```bash
+# Generate synthetic patients
+./run_synthea -p 50000 --exporter.csv.export=true
+
+# Transform to instruction format
+python synthea_to_instructions.py --input ./synthea/output/csv --output ./data
+```
+
+## Limitations
+
+- **Synthetic data**: Does not capture all real-world clinical complexity
+- **US-based demographics**: Synthea defaults to US population characteristics
+- **Simplified risk model**: Does not include family history, lifestyle factors, or genetic risk
+- **English only**: All text is in English
+- **No longitudinal reasoning**: Each example is a snapshot, not a time-series
+
+## Changelog
+
+### v1.0.0 (April 2026)
+- Initial release
+- ~50,000 synthetic patients
+- Diabetes and hypertension risk assessment
+- Train/val/test splits (80/10/10)
+
+## Roadmap
+
+Planned improvements (contributions welcome!):
+
+- [ ] **Ghana/African demographics**: Custom Synthea config for African population characteristics
+- [ ] **Additional NCDs**: Chronic kidney disease, cardiovascular disease, obesity
+- [ ] **Multilingual**: French, Twi, Hausa translations for West African context
+- [ ] **Longitudinal examples**: Multi-visit patient trajectories
+- [ ] **Family history**: Incorporate genetic risk factors
+- [ ] **Lifestyle factors**: Diet, exercise, smoking, alcohol
+- [ ] **Validated models**: Release fine-tuned Gemma/Llama checkpoints
+
+## Contributing
+
+We welcome contributions! Here's how you can help:
+
+1. **Report issues**: Found an error in the data? [Open an issue](https://huggingface.co/datasets/samwell/synthea-ncd-instructions/discussions)
+2. **Improve generation**: Submit PRs to the generation scripts
+3. **Add demographics**: Help create Synthea configs for other regions
+4. **Validate clinically**: Are you a clinician? Help us review the risk assessments
+5. **Translate**: Help translate to other languages
+
+## Citation
+
+```bibtex
+@dataset{synthea_ncd_instructions_2026,
+  title={Synthea NCD Instructions: A Synthetic Dataset for Clinical Risk Assessment},
+  author={samwell},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/samwell/synthea-ncd-instructions},
+  note={Living dataset - check for updates}
+}
+```
+
+## License
+
+Apache 2.0 - free to use, modify, and distribute with attribution.
+
+## Acknowledgments
+
+- [Synthea](https://github.com/synthetichealth/synthea) - Synthetic patient generation
+- [Unsloth](https://github.com/unslothai/unsloth) - Efficient fine-tuning
+- Clinical guidelines: ADA, ACC/AHA, WHO
+- HopeOS team for the initial implementation
+
+## Contact
+
+- **Maintainer**: [@samwell](https://huggingface.co/samwell)
+- **Discussions**: [Dataset discussions](https://huggingface.co/datasets/samwell/synthea-ncd-instructions/discussions)
+- **Issues**: Report data quality issues in discussions
+
+---
+
+*Last updated: April 2026*

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