Synthetic Healthcare Admissions Dataset

🏥 Synthetic Healthcare Admissions Dataset

The Synthetic Healthcare Admissions dataset is a synthetically generated healthcare dataset that mimics patient hospital admission records. It is designed to provide researchers, data scientists, and machine learning practitioners with realistic healthcare data while preserving patient privacy and avoiding exposure of sensitive information.

📂 Dataset Overview

• Type: Tabular / Structured data
• Domain: Healthcare, Electronic Health Records (EHR)
• Content: Synthetic hospital admission records
• Use Cases:
  • Predictive modeling of patient outcomes
  • Length of stay estimation
  • Readmission prediction
  • Resource allocation & optimization in healthcare
  • Experimentation with ML models without privacy risks

⚙️ Features (common fields included in admissions data)

• Patient Demographics: Age, Gender, Ethnicity
• Admission Details: Admission type, Admission date, Discharge date
• Clinical Data: Diagnosis codes (ICD-like), Procedures, Comorbidities
• Hospital Metrics: Length of stay, Department/Unit info
• Synthetic Identifiers: Randomized patient IDs

✅ Why Synthetic?

Real healthcare data is heavily restricted due to HIPAA and GDPR compliance. This dataset provides a privacy-safe alternative, allowing open research while maintaining the structure and statistical properties of real hospital admissions data.

🔬 Applications

• Benchmarking healthcare ML models
• Developing explainable AI solutions in clinical settings
• Testing NLP/ML pipelines for structured EHR data
• Teaching and training purposes