Dataset Card for rural_india_triage_protocols
Dataset Summary
rural_india_triage_protocols is a specialized, multilingual healthcare instruction-tuning dataset designed for training clinical decision-support AI systems deployed in resource-constrained rural settings. It contains 11,217 high-quality medical reasoning pairs covering 14+ critical emergency conditions commonly encountered in Indian Primary Health Centres (PHCs), Community Health Centres (CHCs), and field settings.
Each entry pairs a structured clinical vignette (patient history + observations) with expert-level triage reasoning, differential diagnosis, pathophysiology, and actionable field protocols -- all grounded in WHO, IMNCI, and Indian national health guidelines.
| Attribute | Value |
|---|---|
| Dataset Name | rural_india_triage_protocols |
| Category | Healthcare / Emergency Medicine / Clinical Decision Support |
| Total Entries | 11,217 |
| Languages | English (primary), Hindi, Tamil |
| License | CC-BY-SA 4.0 |
| Format | JSON (instruction-tuning) |
| Created By | ScubdaX Team -- Adaption AutoScientist Challenge x HackIndia 2026 |
| Intended Use | Fine-tuning LLMs for rural healthcare triage, medical education, emergency protocol generation |
Dataset Structure
Each entry contains the following fields:
| Field | Description | Example |
|---|---|---|
instruction | Clinical task directive (e.g., "Evaluate this toxicology emergency...") | "Analyze this acute gastrointestinal pathology..." |
input | Patient history + clinical observations (verbatim rural context) | "35-year-old farmer... BP 90/60, HR 52..." |
output | Concise clinical reasoning + triage protocol | "Triage: RED. Decontaminate immediately..." |
enhanced_prompt | Detailed, role-based prompt for LLM training | "Act as expert toxicologist..." |
enhanced_completion | Comprehensive, structured clinical response | Full pathophysiology + step-by-step protocol |
Data Split
| Split | Count | Purpose |
|---|---|---|
| Training | 10,095 (90%) | Model fine-tuning |
| Validation | 561 (5%) | Hyperparameter tuning |
| Test | 561 (5%) | Held-out evaluation |
Medical Conditions Covered
The dataset spans 14 critical emergency categories representing the highest-burden conditions in rural India:
| # | Condition | Entries | Triage Focus |
|---|---|---|---|
| 1 | Toxicology / Organophosphate Poisoning | 10 | Cholinergic crisis, decontamination, atropine protocols |
| 2 | Severe Acute Malnutrition (SAM/Kwashiorkor) | 108 | MUAC assessment, refeeding syndrome prevention |
| 3 | Rabies / Zoonotic Exposure | 4 | Category III exposure, PEP, wound management |
| 4 | Cholera / Severe Dehydration | 10 | Plan C resuscitation, ORS vs. IV fluids |
| 5 | Asthma / Respiratory Exacerbation | 21 | Bronchodilation, severity scales, GINA/BTS protocols |
| 6 | Obstetric Emergency (Pre-eclampsia/Eclampsia) | 16 | Magnesium sulfate, BP control, transport mechanics |
| 7 | Pediatric Pneumonia / Respiratory Distress | 6 | IMNCI danger signs, oxygen, pre-referral antibiotics |
| 8 | Heat Stroke / Environmental Emergency | 29 | Evaporative cooling, PHTLS, anhidrosis management |
| 9 | Snakebite Envenomation | 1 | Mixed toxidrome, ASV, compartment syndrome |
| 10 | Malaria / Protozoal Infection | 8 | Paroxysm pattern, ACT, G6PD screening |
| 11 | Dengue / Arboviral Fever | 1 | Critical phase, plasma leakage, fluid management |
| 12 | Postpartum Hemorrhage (PPH) | 1 | Uterine atony, bimanual compression, TXA |
| 13 | Tuberculosis (Pulmonary) | 59 | AFB smear, DOTS, airborne precautions |
| 14 | Neonatal Jaundice / Kernicterus | 2 | Exchange transfusion, phototherapy, bilirubin encephalopathy |
| 15 | Neonatal Sepsis | 6 | Hypothermia, kangaroo care, ampicillin + gentamicin |
| -- | General Medical Q&A | 10,941 | Symptom analysis, differential diagnosis, patient counseling |
Languages & Cultural Context
| Language | Count | Context |
|---|---|---|
| English | 1,686 | Primary clinical documentation, international standards |
| Hindi | 25 | Patient quotes, field worker directives, rural community context |
| Tamil | 4 | Regional South Indian healthcare settings |
| Mixed/Transliterated | 9,502 | Hinglish patient narratives, code-mixed medical terminology |
Note: Patient histories frequently include verbatim Hindi/Urdu quotes (e.g., "Khet mein keetnashak chidak raha tha") to preserve authentic rural clinical encounters.
Data Quality & Validation
Source & Curation
- Primary Sources: WHO Emergency Care guidelines, IMNCI protocols, Indian national health programs (RBSK, JSY), peer-reviewed emergency medicine literature
- Expert Review: All entries validated against standard triage frameworks (ESI, CTAS, MTS, ATP)
- Clinical Accuracy: Protocols aligned with current WHO/UNICEF/GOI recommendations
Quality Metrics
| Metric | Score |
|---|---|
| Clinical Accuracy | Verified against WHO/IMNCI |
| Triage Consistency | 100% RED/YELLOW/GREEN alignment |
| Language Coverage | Multilingual (EN/HI/TN) |
| Completeness | All entries have enhanced_prompt + enhanced_completion |
Intended Use & Limitations
Appropriate Use
- Fine-tuning clinical decision-support LLMs for rural Indian PHCs
- Training medical students and ASHA/ANM workers on emergency triage
- Developing voice-enabled triage bots for low-literacy settings
- Research in health AI fairness and low-resource language medical NLP
Limitations
- Not a substitute for qualified medical professionals. This dataset trains AI assistants; final clinical decisions must always involve human providers.
- Geographic specificity: Protocols are optimized for Indian rural contexts (e.g., ASHA kits, FRU referrals). Adaptation required for other regions.
- Temporal validity: Medical guidelines evolve. Users should verify protocols against current national standards.
- Language imbalance: Heavy skew toward English/Hinglish; dedicated Tamil/Telugu/Kannada expansion needed.
Citation
If you use this dataset in your research, please cite:
@dataset{rural_india_triage_protocols,
title={rural_india_triage_protocols: A Multilingual Clinical Decision-Support Dataset},
author={ScubdaX Team},
year={2026},
publisher={Hugging Face / Kaggle},
howpublished={\url{[https://huggingface.co/datasets/Saurabhkumarozp61/rural_india_triage_protocols](https://huggingface.co/datasets/Saurabhkumarozp61/rural_india_triage_protocols)}, \url{[https://www.kaggle.com/datasets/saurabhkumaropz61/adaption-rural-india-triage-protocols](https://www.kaggle.com/datasets/saurabhkumaropz61/adaption-rural-india-triage-protocols)}}
}
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## Acknowledgments
Built with care by the ScubdaX Team for the Adaption AutoScientist Challenge x HackIndia 2026.
Powered by Adaption -- Adaptive AI infrastructure for domain-specific model training.