Synthetic data & the future of Women's Health: A synergistic relationship

Delanerolle，Gayathri1; Phiri，Peter1,2; Cavalini，Heitor1; Benfield，David1,3; Shetty，Ashish4,5; Bouchareb，Yassine6; Shi，Jian Qing1,7; Zemkoho，Alain1,3,8

2023-11-01

10.1016/j.ijmedinf.2023.105238

International Journal of Medical Informatics   影响因子和分区

1386-5056

1872-8243

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.

Electronic health records Machine learning Real-world Data Synthetic data

SCI ; EI

英语

其他

Computer Science ; Health Care Sciences & Services ; Medical Informatics

Computer Science, Information Systems ; Health Care Sciences & Services ; Medical Informatics

WOS:001090925500001

ELSEVIER IRELAND LTD

20234114855015

Data Science ; E-learning ; Ethical technology ; Population statistics

Artificial Intelligence:723.4 ; Engineering Professional Aspects:901.1 ; Information Sources and Analysis:903.1

CLINICAL MEDICINE

2-s2.0-85173286317

Scopus

1.Research & Innovation Department,Southern Health NHS Foundation Trust,Southampton,SO40 2RZ,United Kingdom
2.School of Psychology,Faculty of Environmental and Life Sciences,University of Southampton,Southampton,SO17 1BJ,United Kingdom
3.Department of Mathematics,University of Southampton,Southampton,SO17 1BJ,United Kingdom
4.Female Pelvic Medicine and Reconstructive Surgery,University College London,London,WC1E 6BT,United Kingdom
5.University College London Hospitals NHS Foundation Trust,London,NW1 2PG,United Kingdom
6.Sultan Qaboos University,College of Medicine and Health Sciences,Muscat,Oman
7.Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen,518055,China
8.Alan Turing Institute,London,96 Euston Road, NW1 2DB,United Kingdom

Delanerolle，Gayathri,Phiri，Peter,Cavalini，Heitor,et al. Synthetic data & the future of Women's Health: A synergistic relationship[J]. International Journal of Medical Informatics,2023,179.

Delanerolle，Gayathri.,Phiri，Peter.,Cavalini，Heitor.,Benfield，David.,Shetty，Ashish.,...&Zemkoho，Alain.(2023).Synthetic data & the future of Women's Health: A synergistic relationship.International Journal of Medical Informatics,179.

Delanerolle，Gayathri,et al."Synthetic data & the future of Women's Health: A synergistic relationship".International Journal of Medical Informatics 179(2023).