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EMI - Urinalysis Patient Records Dataset (Synthetic)
Patient Health Records & Digital Health
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£199.99
About
The EMI - Urinalysis Patient Records Dataset (Synthetic) is created for educational and research purposes to analyze various factors associated with urinalysis results and their relation to patient health. This dataset includes anonymized, synthetic data on clinical factors and lab results for patients, providing insights into urinary characteristics and diagnoses.
Dataset Features
- Age: Age of the patient (in years).
- Gender: Gender of the patient (Male/Female).
- Color: The color of the urine sample (e.g., Light Yellow, Yellow, Dark Yellow, etc.).
- Transparency: Transparency of the urine sample (e.g., Clear, Slightly Hazy).
- Glucose: Presence of glucose in the urine (e.g., Negative, Trace, 1+, 2+).
- Protein: Presence of protein in the urine (e.g., Negative, Trace, 1+, 2+).
- pH: pH level of the urine.
- Specific Gravity: The specific gravity of the urine sample.
- WBC (White Blood Cells): Number of white blood cells in the urine sample.
- RBC (Red Blood Cells): Number of red blood cells in the urine sample.
- Epithelial Cells: Presence of epithelial cells (e.g., Few, Rare, Moderate, Loaded).
- Mucous Threads: Presence of mucous threads (e.g., Few, Rare, None Seen).
- Amorphous Urates: Presence of amorphous urates (e.g., Few, Rare, None Seen).
- Bacteria: Presence of bacteria in the urine (e.g., Few, Moderate, Loaded).
- Diagnosis: The diagnosis result based on the urinalysis (e.g., Positive, Negative).
Distribution
Usage
This dataset can be used for the following applications:
- Healthcare Analytics: Investigate the relationships between urinalysis results and common health conditions.
- Predictive Modeling: Build machine learning models to predict conditions like urinary tract infections, kidney disease, and other related health issues based on urinalysis results.
- Clinical Research: Study the impact of various factors such as glucose, protein, and pH levels on patient diagnoses.
- Educational Purposes: Provide a resource for students and practitioners in healthcare, data science, and laboratory medicine fields to analyze real-world clinical data.
Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It contains diverse factors such as urine characteristics, laboratory test results, and diagnoses that are representative of typical clinical practice.
License
CC0 (Public Domain)
Who Can Use It
- Healthcare Researchers: To explore correlations between urine test results and various health conditions.
- Clinicians and Medical Practitioners: To understand how specific urinalysis indicators relate to diagnoses.
- Data Scientists and Machine Learning Practitioners: To create models that predict health conditions based on urinalysis results.
- Educators and Students: As a resource for studying clinical and healthcare data analysis.
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