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Spot the difference: comparing results of analyses from real patient data and synthetic derivatives
- Source :
- JAMIA Open
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
Abstract
- Background Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. Objectives To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. Methods We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). Results For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. Discussion and conclusion This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.
- Subjects :
- AcademicSubjects/SCI01060
Process (engineering)
Computer science
data analysis
Health Informatics
Research and Applications
computer.software_genre
01 natural sciences
Synthetic data
electronic health records and systems
03 medical and health sciences
0302 clinical medicine
Robustness (computer science)
Use case
Confidentiality
030212 general & internal medicine
0101 mathematics
protected health information
Protected health information
precision health care
010102 general mathematics
synthetic data
Data sharing
Key (cryptography)
Data mining
AcademicSubjects/SCI01530
AcademicSubjects/MED00010
computer
Subjects
Details
- ISSN :
- 25742531
- Volume :
- 3
- Database :
- OpenAIRE
- Journal :
- JAMIA Open
- Accession number :
- edsair.doi.dedup.....5b91c96132bd4a3c33f5528c87bcdf3b