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Synthetic Healthcare Dataset

Healthcare Providers & Services Utilization

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Synthetic

Healthcare

Dataset

Patient

Records

AI

LLM

Training

Trusted By
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£19.99

About

This synthetic healthcare dataset has been generated to serve as an educational resource for data science, machine learning, and data analysis applications in the healthcare industry. It mirrors real-world patient records, allowing users to practice data manipulation and develop analytical skills in a healthcare context.

Dataset Features:

  • Age: Age of the patient at admission (in years).
  • Gender: Gender of the patient.
  • Blood Type: Patient's blood type (e.g., "A+", "O-").
  • Medical Condition: Primary diagnosis or medical condition, such as "Diabetes," "Hypertension," or "Asthma."
  • Date of Admission: The date when the patient was admitted.
  • Insurance Provider: Name of the patient’s insurance provider (e.g., "Aetna," "Cigna").
  • Billing Amount: Total charges for healthcare services provided (in monetary units).
  • Room Number: Room number assigned to the patient during their stay.
  • Admission Type: Type of admission, categorized as "Emergency," "Elective," or "Urgent."
  • Discharge Date: Date when the patient was discharged.
  • Test Results: Outcome of a medical test, categorized as "Normal," "Abnormal," or "Inconclusive."

Visualisation:

Distribution

Usage:

This dataset can be used for:

  • Healthcare research: To explore trends and patterns in patient admissions, conditions, and outcomes.
  • Educational training: To teach data cleaning, transformation, and visualization techniques specific to healthcare data.
  • Predictive modelling: To develop models that predict healthcare outcomes based on various patient and admission factors.

Coverage:

This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.

License:

CCO (Public Domain)

Who can use it:

  • Researchers and educators: For studies or teaching purposes in healthcare analytics and data science.
  • Data science enthusiasts: For learning, practising, and applying healthcare data manipulation and analysis techniques.

Dataset Information

VIEWS

46

DOWNLOADS

0

LICENSE

CC0

REGION

GLOBAL

TYPE

Textual

VERSION

1