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Predictive Oxygen Flow Model Data
Data Science and Analytics
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About
Detailed patient data designed for analyzing oxygen demand in individuals suffering from acute respiratory syndromes. The primary goal is to determine the doctor-prescribed instantaneous oxygen flow concentration based on real-time physiological indicators. The dataset includes demographics, oxygen saturation levels, and pulse rates, offering a powerful resource for building prediction models related to respiratory support requirements. This resource was synthesised using the CTGAN of the SVD package to ensure patient privacy while maintaining statistical integrity.
Columns
- Age: The patient's exact age, measured in years. The age range spans from 17 to 100.
- Gender: The patient's specific gender, encoded categorically.
- SPO2 (Instantaneous Oxygen Saturation): The patient's oxygen saturation percentage at the time of measurement, ranging from 35% to 99%.
- PR (Instantaneous Pulse Rate): The patient's pulse rate at the time of measurement, ranging from 40 to 110.
- C/NC (Categorical Value for nCoV2 Infection): A binary category indicating whether the patient is infected with SARS nCoV2 (1.0) or not (0.0).
- Oxygen Flow (Target Variable): The doctor-prescribed instantaneous oxygen flow concentration for the patient, which is the key variable this data is designed to predict.
Distribution
The dataset represents a real-time analysis containing 200,000 records, or patient entries. It consists of six columns, including five features and one target variable, and is typically provided in a CSV file format. The file size is approximately 4.35 MB. Data statistics indicate that some columns, such as SPO2, PR, C/NC, and Oxygen Flow, have missing values ranging from 13% to 19% of the total records. Updates to this dataset are expected to occur on an annual basis.
Usage
This data is ideally suited for machine learning applications, particularly classification and regression tasks focused on predictive modelling.
Potential applications include:
- Developing models to predict the required oxygen flow concentration based on patient vitals.
- Real-time clinical decision support systems for respiratory care.
- Analyzing demographic and health condition factors that correlate with high oxygen demand.
- Applying advanced techniques like Gradient Boosting for high-accuracy predictions.
Coverage
The data provides medical information spanning a defined period of time and duration, focusing on 200,000 unique patients. Patient age coverage ranges widely, from 17 to 100 years. The scope is limited to patient data related to acute respiratory syndromes and their corresponding oxygen requirements, including categorization based on SARS nCoV2 infection status.
License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Who Can Use It
- Healthcare Researchers: To analyze patterns in oxygen demand across different patient categories and demographics.
- Data Scientists and Machine Learning Engineers: To train, test, and validate predictive models for clinical support.
- Medical Policy Analysts: To understand resource allocation and expected oxygen needs for large patient cohorts.
Dataset Name Suggestions
- Real-Time Respiratory Oxygen Demand
- Predictive Oxygen Flow Model Data
- Acute Respiratory Syndrome Patient Vitals
- SPO2 and Pulse Rate Predictors
Attributes
Original Data Source:Predictive Oxygen Flow Model Data
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