Identifying and handling data bias within primary healthcare data using synthetic data generators.

Area of Science:

• Medical Informatics
• Artificial Intelligence
• Data Science

Background:

• Advanced synthetic data generators can simulate sensitive patient data, reducing identification risks and enabling AI development in medicine.
• Massive datasets like UK-NHS records are available, but biases from under-represented cohorts can transfer to synthetic data.
• Machine learning models can perpetuate data biases, leading to inaccurate correlations and distributions in synthetic datasets.

Purpose of the Study:

• To enhance synthetic data generators by addressing bias and improving predictive model performance.
• To introduce probabilistic methods for detecting and boosting difficult-to-predict samples in ground truth data.
• To develop strategies for generating bias-reduced synthetic data that also boosts AI model accuracy.

Main Methods:

• Probabilistic approaches to identify challenging data samples within ground truth datasets.
• Techniques to "boost" these difficult samples during the synthetic data generation process.
• Exploration of bias reduction strategies integrated with performance enhancement for predictive models.

Main Results:

• Improved detection of under-represented or complex data points in original datasets.
• Enhanced synthetic data generation that mitigates bias propagation.
• Demonstrated potential for synthetic data to improve the accuracy and fairness of AI diagnostic and management tools.

Conclusions:

• Probabilistic methods can effectively identify and address biases in synthetic data generation.
• The proposed techniques improve the quality of synthetic data for medical AI applications.
• This approach facilitates the development of more robust, equitable, and accurate AI-driven healthcare solutions.