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Synthetic Cirrhosis Patient Survival Dataset
Patient Health Records & Digital Health
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£199.99
About
The Synthetic Cirrhosis Patient Survival Dataset is designed for educational and research purposes to analyze patient survival and clinical conditions related to cirrhosis. It provides anonymized, synthetic data on various clinical factors, laboratory values, and treatment regimens for patients diagnosed with cirrhosis.
Dataset Features
- ID: Unique identifier for the patient.
- N_Days: The number of days the patient survived after diagnosis.
- Status: The survival status of the patient (C = Survived, D = Deceased, CL = Censored).
- Drug: The drug prescribed to the patient (D-penicillamine, Placebo).
- Age: Age of the patient (in years).
- Sex: The sex of the patient (M/F).
- Ascites: Whether the patient has ascites (Y/N).
- Hepatomegaly: Whether the patient has hepatomegaly (Y/N).
- Spiders: Whether the patient has spider angiomas (Y/N).
- Edema: Whether the patient has edema (Y/N).
- Bilirubin: Bilirubin level in the patient's blood (mg/dL).
- Cholesterol: Cholesterol level in the patient's blood (mg/dL).
- Albumin: Albumin level in the patient's blood (g/dL).
- Copper: Copper level in the patient's blood (µg/dL).
- Alk_Phos: Alkaline phosphatase level in the patient's blood (U/L).
- SGOT: Serum Glutamic-Oxaloacetic Transaminase (SGOT) level in the patient's blood (U/L).
- Tryglicerides: Triglycerides level in the patient's blood (mg/dL).
- Platelets: Platelet count (x10^3/µL).
- Prothrombin: Prothrombin time in seconds.
- Stage: The stage of cirrhosis (1-4).
Distribution
Usage
This dataset can be used for the following applications:
- Healthcare Analytics: Identify clinical factors associated with cirrhosis progression and survival.
- Predictive Modeling: Develop machine learning models to predict cirrhosis patient survival outcomes.
- Clinical Research: Investigate the impact of different drugs and clinical factors on cirrhosis survival.
- Educational Purposes: Provide a dataset for students in healthcare, medical, data science, and public health fields to analyze real-world clinical trends.
Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a variety of clinical and laboratory factors to support a broad range of research and analysis.
License
CC0 (Public Domain)
Who Can Use It
- Healthcare Researchers: To explore correlations between clinical factors and cirrhosis survival.
- Clinicians and Medical Practitioners: To analyze the impact of drugs and clinical features on cirrhosis progression.
- Data Scientists and Machine Learning Practitioners: To develop predictive models for cirrhosis survival.
- Educators and Students: As a resource for studying healthcare analytics and medical research.
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