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Synthetic Infant Health Monitoring Dataset, First 30 Days
Patient Health Records & Digital Health
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£179.99
About
This synthetic Infant Health Dataset is designed for educational and research purposes in the fields of data science, pediatrics, and healthcare analytics. It contains critical health indicators of infants such as birth asphyxia, oxygen levels, chest X-ray findings, and other vital parameters. The dataset is ideal for building predictive models, conducting risk assessments, and exploring relationships between early health indicators and infant disease outcomes.
Dataset Features
- BirthAsphyxia: Whether the infant experienced birth asphyxia (Yes/No).
- HypDistrib: Hypoxia distribution categorized as Equal or Unequal.
- HypoxiaInO2: Severity of hypoxia in oxygen levels (Moderate/Severe).
- CO2: Carbon dioxide levels in the blood (Normal/Abnormal).
- ChestXray: Results of the chest X-ray (Normal/Plethoric/Oligaemic/Asy/Patchy).
- Grunting: Whether the infant exhibits grunting (Yes/No).
- LVHreport: Left Ventricular Hypertrophy (LVH) reported (Yes/No).
- LowerBodyO2: Oxygen levels in the lower body (<5, 5–12, >12).
- RUQO2: Oxygen levels in the right upper quadrant (<5, 5–12, >12).
- CO2Report: Classification of CO2 levels (>=7.5, <7.5).
- XrayReport: Findings in the chest X-ray report (Normal, Asy/Patchy, Oligaemic).
- Disease: The diagnosed disease (e.g., PAIVS, Fallot).
- GruntingReport: Detailed report on grunting (Yes/No).
- Age: Age of the infant (e.g., 0–3 days, 4–10 days).
- LVH: Left Ventricular Hypertrophy status (Yes/No).
- DuctFlow: Status of duct flow (e.g., None, Lt_to_Rt).
- CardiacMixing: Degree of cardiac mixing (e.g., Complete, Mild, Transp.).
- LungParench: Condition of the lung parenchyma (Normal/Abnormal).
- LungFlow: Flow levels in the lungs (Low/Normal/High).
- Sick: Whether the infant is sick (Yes/No).
Distribution
Usage
This dataset is ideal for various applications in infant healthcare:
- Disease Risk Prediction: Develop machine learning models to predict infant health outcomes based on medical indicators.
- Health Indicator Analysis: Identify critical factors contributing to infant diseases and prioritize early interventions.
- Predictive Modeling: Build predictive models using health metrics to assess infant health risks.
- Pediatric Research: Explore relationships between early health indicators and disease diagnoses.
- Preventive Healthcare: Inform healthcare policies and clinical practices to improve infant health outcomes.
Coverage
This synthetic dataset is anonymized, ensuring compliance with data privacy standards. It provides a diverse set of infant health conditions and parameters for research and educational purposes.
License
CC0 (Public Domain)
Who Can Use It
- Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to infant health.
- Healthcare Professionals and Researchers: To explore relationships between early health indicators and disease outcomes.
- Pediatricians and Neonatologists: To understand trends and develop strategies for improving neonatal care.
- Public Health Analysts: To study and recommend interventions for reducing infant disease risks.
- Policy Makers and Regulators: For data-driven decision-making in neonatal and pediatric healthcare policies.
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