--- 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)} } ```