Synthetic Longitudinal Dataset Generation for Clinical Trial Privacy
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Solution Overview
Problem
Clinical trial data from Electronic Data Capture (EDC) systems pose privacy concerns due to their sensitive nature, limiting accessibility and hindering research, as conventional de-identification and anonymization techniques are inadequate for ensuring patient privacy, especially with smaller dataset sizes.
Innovation Solution
A method to generate high-fidelity synthetic longitudinal EDC data by embedding feature vectors in a lower dimension space, identifying nearest neighbors, and swapping subsequence patterns, ensuring statistical similarity while maintaining privacy, thus allowing researchers to analyze data without exposing individual patient information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional de-identification and anonymization techniques are applied to EDC data, then data accessibility is improved, but patient privacy is compromised
Solution Approach 1:
The patent generates synthetic copies of clinical trial data that replicate the statistical properties and patterns of original EDC data without containing actual patient information. This copying approach allows researchers to access and analyze data while patient privacy is preserved, as the synthetic data cannot be traced back to individual patients
Solution Approach 2:
The patent introduces synthetic data as an intermediary between the original sensitive EDC data and research applications. This intermediary layer enables data utilization while maintaining privacy barriers, allowing researchers to work with data that mimics the structure and characteristics of real clinical trial data without direct access to sensitive patient information
2Object-affected harmful factors
If deep learning techniques like GANs and VAEs are used to generate synthetic data, then privacy control is improved, but data fidelity deteriorates due to small dataset sizes
Solution Approach 1:
The patent changes the approach from deep learning parameter optimization to statistical parameter preservation. It focuses on maintaining key statistical properties (distribution characteristics, event frequencies, temporal patterns) of the original data while generating synthetic samples, which is particularly effective for small clinical trial datasets where deep learning would overfit or fail to learn meaningful patterns
3Productivity
If EDC data is made broadly available for research, then research value is improved, but regulatory and privacy compliance becomes more difficult
Solution Approach 1:
The patent creates synthetic copies of EDC data that can be freely distributed and used for research purposes without regulatory restrictions. These synthetic datasets maintain the scientific value and statistical properties needed for research while eliminating regulatory and privacy compliance barriers, as they contain no actual patient information
Data Source
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Figure 3A~3B
AI summary
Generating a synthetic longitudinal dataset includes identifying subsequence patterns in records defining event sequences for patients. Feature vectors are determined, each characterizing a corresponding one of the records, based on the subsequence patterns. The feature vectors are embedded in a lower dimension space. A seed record is iteratively selected from among the records and in each iteration: subsequence patterns are identified in a subset of the records. Instances of subsequence patterns in the seed record are replaced with instances of similar subsequence patterns identified in the subset of the records to form a modified seed record. The iterations are repeated until all of the records have been selected as the seed record. The modified seed records are combined to form the synthetic dataset.