Synthetic data & the future of women’s health: a synergistic relationship

Delanerolle, Gayathri, Phiri, Peter, Cavalini, Heitor, Benfield, David, Shetty, Ashish, Bouchareb, Yassine, Shi, Jian Qing and Zemkoho, Alain (2023) Synthetic data & the future of women’s health: a synergistic relationship. International Journal of Medical Informatics, 179, [105238]. (doi:10.1016/j.ijmedinf.2023.105238).

Abstract

Objectives: the aim of this perspective is to report the use of synthetic data as a viable method in women’s health given the current challenges linked to obtaining life-course data within a short period of time and accessing electronic healthcare data.

Methods: we used a 3-point perspective method to report an overview of data science, common applications, and ethical implications.

Results: there are several ethical challenges linked to using real-world data, consequently, generating synthetic data provides an alternative method to conduct comprehensive research when used effectively. The use of clinical characteristics to develop synthetic data is a useful method to consider. Aligning this data as closely as possible to the clinical phenotype would enable researchers to provide data that is very similar to that of the real-world.

Discussion: population diversity and disease characterisation is important to optimally use data science. There are several artificial intelligence techniques that can be used to develop synthetic data.

Conclusion: synthetic data demonstrates promise and versatility when used efficiently aligned to clinical problems. Therefore, exploring this option as a viable method in women’s health, in particular for epidemiology may be useful.

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Contributors

Author: Alain Zemkoho

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