Synthetic data production for biomedical research
10.24171/j.phrp.2024.0335
- Author:
Yun Gyeong LEE
1
;
Mi-Sook KWAK
;
Jeong Eun KIM
;
Min Sun KIM
;
Dong Un NO
;
Hee Youl CHAI
Author Information
1. Department of Future Healthcare, Korea National Institute of Health, Cheongju, Republic of Korea
- Publication Type:Data Profile
- From:
Osong Public Health and Research Perspectives
2025;16(2):94-99
- CountryRepublic of Korea
- Language:English
-
Abstract:
Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.
