Preprint Communication Version 1 Preserved in Portico This version is not peer-reviewed

Synthetic Data & the Future of Women's Health: A Synergistic Relationship

Gayathri Delanerolle
,
Peter Phiri
* ORCID logo ,
Heitor Cavalini
,
David Benfield
,
Ashish Shetty
,
Yassine Bouchareb
,
Jian Shi
,
Alain Zemkoho
Version 1 : Received: 23 May 2023 / Approved: 24 May 2023 / Online: 24 May 2023 (04:48:58 CEST)

A peer-reviewed article of this Preprint also exists.

Delanerolle, G.; Phiri, P.; Cavalini, H.; Benfield, D.; Shetty, A.; Bouchareb, Y.; Qing Shi, J.; Zemkoho, A. Synthetic Data & the Future of Women’s Health: A Synergistic Relationship. International Journal of Medical Informatics 2023, 105238, doi:10.1016/j.ijmedinf.2023.105238. Delanerolle, G.; Phiri, P.; Cavalini, H.; Benfield, D.; Shetty, A.; Bouchareb, Y.; Qing Shi, J.; Zemkoho, A. Synthetic Data & the Future of Women’s Health: A Synergistic Relationship. International Journal of Medical Informatics 2023, 105238, doi:10.1016/j.ijmedinf.2023.105238.

Abstract

Abstract ObjectivesThe 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. ConclusionSynthetic 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.

Keywords

Womens Health; Data Science; Data Methods; Artificial Intelligence

Subject

Medicine and Pharmacology, Clinical Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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