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Datasets:
PatSnap
/
medical-institution

Tasks:
Text Retrieval
Modalities:
Tabular
Text
Formats:
json
Size:
10K - 100K
Tags:
healthcare-infrastructure
hospitals
entity-resolution
knowledge-graph
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
license: cc-by-4.0
pretty_name: Global Medical Institutions & Hierarchy Graph
tags:
  - healthcare-infrastructure
  - hospitals
  - entity-resolution
  - knowledge-graph
task_categories:
  - text-retrieval
size_categories:
  - 10K<n<100K

🏛️ Dataset Summary

A global directory of medical institutions and organizations covering 14,065 entities. Provides basic organizational profiles with drug pipeline association counts.

🚀 Key Features

  • Global Coverage: country field spans institutions worldwide.
  • Drug Association Count: total_drug indicates the number of drugs associated with each institution.
  • Organization Size: employee_number available for ~49% of records.

💻 Quick Start & MCP Integration

from datasets import load_dataset

dataset = load_dataset("your-org/medical-institutions", split="train")
print(dataset[0]["name"])
# Output: 'Unlimited Bio'
print(dataset[0]["country"])
# Output: 'Honduras'
print(dataset[0]["total_drug"])
# Output: 1

🔗 Resources