Dry Eye Disease Patient Records (Synthetic)
Patient Health Records & Digital Health
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£179.99
About
The Dry Eye Disease Patient Records (Synthetic) is designed for educational and research purposes to analyze patterns in sleep behavior, stress levels, lifestyle factors, and their potential links to dry eye disease. It provides anonymized, synthetic data on various health conditions and behavioral habits.
Dataset Features
- Gender: Gender of the individual (Male/Female).
- Age: Age of the individual.
- Sleep Duration: Average sleep duration in hours.
- Sleep Quality: Subjective assessment of sleep quality (scale-based).
- Stress Level: Measured stress level (scale-based).
- Heart Rate: Resting heart rate (bpm).
- Daily Steps: Number of steps taken per day.
- Physical Activity: Minutes of physical activity per day.
- Height & Weight: Individual’s height (cm) and weight (kg).
- Sleep Disorder: Presence of a diagnosed sleep disorder (Yes/No).
- Wake Up During Night: Frequency of waking up during the night (Yes/No).
- Feel Sleepy During Day: Self-reported daytime sleepiness (Yes/No).
- Caffeine Consumption: Frequency of caffeine intake (Yes/No).
- Alcohol Consumption: Frequency of alcohol intake (Yes/No).
- Smoking: Smoking habits (Yes/No).
- Medical Issue: Presence of any medical conditions (Yes/No).
- Ongoing Medication: Use of any ongoing medication (Yes/No).
- Smart Device Before Bed: Usage of smart devices before sleeping (Yes/No).
- Average Screen Time: Daily screen time in hours.
- Blue-Light Filter: Use of blue-light filters on devices (Yes/No).
- Eye Discomfort & Strain: Presence of discomfort and eye strain (Yes/No).
- Redness in Eye: Occurrence of eye redness (Yes/No).
- Itchiness/Irritation in Eye: Symptoms of eye itchiness or irritation (Yes/No).
- Dry Eye Disease: Diagnosis of Dry Eye Disease (Yes/No).
Distribution
Usage
This dataset can be used for the following applications:
- Healthcare Analytics: Identify patterns between lifestyle factors and dry eye disease.
- Predictive Modeling: Develop machine learning models to predict eye health risks.
- Clinical Research: Investigate associations between screen time, sleep habits, and eye conditions.
- Educational Purposes: Provide a dataset for students in medical, data science, and public health fields to analyze real-world health trends.
Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a variety of demographic and lifestyle factors to support a broad range of research and analysis.
License
CC0 (Public Domain)
Who Can Use It
- Healthcare Researchers: To explore correlations between lifestyle habits and dry eye disease.
- Clinicians and Medical Practitioners: To analyze factors contributing to eye health issues.
- Data Scientists and Machine Learning Practitioners: To develop predictive models for eye-related conditions.
- Educators and Students: As a resource for studying health analytics and medical research.
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