README.md

---
language:
- en
license: mit
task_categories:
- tabular-classification
- text-classification
- feature-extraction
tags:
- medical
- pharmacy
- healthcare
- drug-recommendation
- symptom-checking
- clinical-intelligence
pretty_name: AYUVANT Clinical Intelligence and Pharmacy Dataset
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - ayuvant_cleaned_dataset.json
---

# 🏥 AYUVANT Clinical Intelligence and Pharmacy Dataset

> **Curated, structured, and continuously maintained via [Adaptive Data](https://adaptivedata.io) — an intelligent dataset management platform that enables versioning, schema validation, and real-time collaboration across clinical data pipelines.**

---

## 📋 Dataset Description

The **AYUVANT Clinical Intelligence and Pharmacy Dataset** is a structured, multi-config dataset designed to power clinical decision-support systems, drug recommendation engines, and pharmacy intelligence tools. It covers the full clinical data lifecycle — from symptom detection through disease mapping to medicine dispensation and transaction logging.

This dataset was built and is actively maintained on **[Adaptive Data](https://adaptivedata.io)**, which provides:

- 🔄 **Adaptive versioning** — every schema change and data update is tracked with full lineage
- ✅ **Automated validation** — JSON configs are validated against clinical ontologies on every push
- 🤝 **Collaborative curation** — multi-contributor workflows with conflict resolution built in
- 📊 **Usage analytics** — real-time monitoring of how each config is consumed downstream

---

## 🗂️ Dataset Configs

This dataset is organized into **7 focused configs**, each representing a distinct clinical domain:

| Config | File | Description |
|---|---|---|
| `diseases` | `diseases.json` | Structured disease ontology with ICD-adjacent categorization |
| `symptoms` | `symptoms.json` | Granular symptom taxonomy linked to disease clusters |
| `medicines` | `medicines.json` | Drug catalog with dosage, category, and indication metadata |
| `salt_compositions` | `salt_compositions.json` | Active pharmaceutical ingredient (API) profiles |
| `side_effects` | `side_effects.json` | Adverse effect registry mapped to medicines and compositions |
| `suspicious_health_issues` | `suspicious_health_issues.json` | Flagged symptom patterns for triage and clinical escalation |
| `transaction_records` | `transaction_records.json` | Anonymized pharmacy dispensation and prescription records |

---

## ⚡ Quickstart

### Load a Single Config

```python
from datasets import load_dataset

# Load the disease ontology
diseases = load_dataset("Anshulpj12/Adaptive_Dataset_med", name="diseases")
print(diseases["train"][0])

# Load the medicine catalog
medicines = load_dataset("Anshulpj12/Adaptive_Dataset_med", name="medicines")
print(medicines["train"][0])
```

### Load All Configs

```python
from datasets import load_dataset

configs = [
    "diseases",
    "symptoms",
    "medicines",
    "salt_compositions",
    "side_effects",
    "suspicious_health_issues",
    "transaction_records",
]

ayuvant = {cfg: load_dataset("Anshulpj12/Adaptive_Dataset_med", name=cfg) for cfg in configs}

# Example: cross-reference symptoms with diseases
for record in ayuvant["symptoms"]["train"]:
    print(record)
```

### Symptom-to-Medicine Pipeline Example

```python
from datasets import load_dataset

symptoms_ds   = load_dataset("Anshulpj12/Adaptive_Dataset_med", name="symptoms")["train"]
diseases_ds   = load_dataset("Anshulpj12/Adaptive_Dataset_med", name="diseases")["train"]
medicines_ds  = load_dataset("Anshulpj12/Adaptive_Dataset_med", name="medicines")["train"]
side_fx_ds    = load_dataset("Anshulpj12/Adaptive_Dataset_med", name="side_effects")["train"]

# Build lookup maps
disease_map  = {d["id"]: d for d in diseases_ds}
medicine_map = {m["id"]: m for m in medicines_ds}

def clinical_lookup(symptom_query: str):
    """Minimal clinical decision-support lookup."""
    matched_symptoms = [
        s for s in symptoms_ds
        if symptom_query.lower() in s.get("name", "").lower()
    ]
    results = []
    for sym in matched_symptoms:
        for disease_id in sym.get("linked_diseases", []):
            disease = disease_map.get(disease_id, {})
            meds    = [medicine_map[m] for m in disease.get("recommended_medicines", [])
                       if m in medicine_map]
            results.append({
                "symptom":  sym["name"],
                "disease":  disease.get("name"),
                "medicines": [m["name"] for m in meds],
            })
    return results

print(clinical_lookup("fever"))
```

---

## 🔬 Intended Use Cases

- **Drug Recommendation Systems** — map symptoms → diseases → medicines with side-effect filtering
- **Clinical Triage Tools** — flag suspicious symptom clusters for escalation
- **Pharmacy Intelligence** — analyse dispensation patterns from transaction records
- **Medical NLP Training** — structured labels for symptom extraction and entity recognition
- **Healthcare Tabular ML** — tabular classification benchmarks on clinical data

---

## 🏗️ Adaptive Data Platform Integration

This dataset is actively managed through **[Adaptive Data](https://adaptivedata.io)**.

### What "Active Usage" Means Here

| Platform Feature | How It's Used in This Dataset |
|---|---|
| **Schema Registry** | Each config's JSON structure is registered and enforced — malformed entries are rejected at ingest |
| **Version Lineage** | Every medicine, disease, or symptom addition is logged with contributor ID and timestamp |
| **Diff Reviews** | Pull-request-style reviews gate any changes to `medicines.json` or `suspicious_health_issues.json` |
| **Automated QA** | On every push, Adaptive Data runs null-check, duplicate-ID, and cross-reference integrity tests |
| **Dataset Snapshots** | Tagged stable releases (e.g., `v1.0`, `v1.1`) are pinned so downstream models can lock to a version |

### Syncing from Adaptive Data

```python
# If you have Adaptive Data CLI configured
# adaptive pull Adaptive_Dataset_med --config medicines --format json

# Or via the Adaptive Data Python SDK
from adaptive_data import AdaptiveClient

client = AdaptiveClient(api_key="YOUR_API_KEY")
dataset = client.dataset("Adaptive_Dataset_med")

medicines_snapshot = dataset.config("medicines").pull(version="latest")
medicines_snapshot.to_json("medicines.json")
```

---

## 📊 Dataset Statistics

| Config | Approximate Records | Format | Updated |
|---|---|---|---|
| diseases | — | JSON array | Tracked via Adaptive Data |
| symptoms | — | JSON array | Tracked via Adaptive Data |
| medicines | — | JSON array | Tracked via Adaptive Data |
| salt_compositions | — | JSON array | Tracked via Adaptive Data |
| side_effects | — | JSON array | Tracked via Adaptive Data |
| suspicious_health_issues | — | JSON array | Tracked via Adaptive Data |
| transaction_records | — | JSON array | Tracked via Adaptive Data |

> Record counts update continuously. See the **Adaptive Data dashboard** for live statistics.

---

## ⚠️ Limitations and Bias

- This dataset is intended for **research and development purposes only** and is **not a substitute for professional medical advice**.
- Drug recommendations and clinical associations reflect the curation logic at time of release and may not capture the latest clinical guidelines.
- Transaction records are fully **anonymized** — no personally identifiable information (PII) is present.
- Coverage is weighted toward common conditions; rare disease representation may be limited.

---

## 📜 License

This dataset is released under the **MIT License**. See `LICENSE` for full terms.

---

## 🙏 Credits and Acknowledgements

**Dataset curation and maintenance platform:**
> [**Adaptive Data**](https://adaptivedata.io) — Intelligent dataset lifecycle management for structured and clinical data. Schema validation, version control, collaborative curation, and automated QA pipelines powering the AYUVANT dataset from ingestion to Hugging Face publication.

**Project:** AYUVANT Clinical Intelligence System
**Maintainer:** *(your name / organisation)*
**Contact:** *(your contact / repo link)*

---

## 📎 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{ayuvant_clinical_2025,
  title     = {AYUVANT Clinical Intelligence and Pharmacy Dataset},
  author    = {Anshul Prajapati},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Anshulpj12/Adaptive_Dataset_med},
  note      = {Curated and maintained via Adaptive Data (https://adaptivedata.io)}
}
```