README.md

---
license: cc-by-4.0
task_categories:
- tabular-classification
language:
- en
tags:
- disability
- stroke
- rehabilitation
- hemiplegia
- physiotherapy
- synthetic
- sub-saharan-africa
pretty_name: Stroke Rehabilitation & Recovery (SSA)
size_categories:
- 10K<n<100K
configs:
- config_name: urban_stroke_unit
  data_files: data/stroke_urban.csv
  default: true
- config_name: district_hospital
  data_files: data/stroke_district.csv
- config_name: rural_community
  data_files: data/stroke_rural.csv
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Stroke Rehabilitation & Recovery in Sub-Saharan Africa

## Abstract

Synthetic dataset modelling stroke types, acute care, post-stroke disability, rehabilitation access, and functional recovery across three care tiers in SSA. 70% of stroke survivors have disability; <5% access rehabilitation in SSA.

## Parameterization Evidence

| Parameter | Value | Source | Year |
| --- | --- | --- | --- |
| Stroke incidence increasing; youngest patients globally | Burden | Lancet Neurology | 2022 |
| 70% survivors disabled; <5% access rehab | Access | WHO | 2023 |
| Physiotherapy <10% coverage in most SSA | Workforce | PMC | 2022 |

## Validation

![Validation Report](validation_report.png)

## Usage

```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/stroke-rehabilitation-recovery", "urban_stroke_unit")
```

## References

1. Lancet Neurology. Stroke in SSA. 2022.
2. WHO. Stroke fact sheet. 2023.

## Citation

```bibtex
@dataset{electricsheepafrica_stroke_rehabilitation_recovery_2025,
  title={Stroke Rehabilitation and Recovery in Sub-Saharan Africa},
  author={Electric Sheep Africa},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/electricsheepafrica/stroke-rehabilitation-recovery}
}
```

## License

CC-BY-4.0