Domain Knowledge-Driven Generation of Synthetic Healthcare Data
(2023) In Studies in Health Technology and Informatics 302. p.352-353- Abstract
- Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility,... (More)
- Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility, fidelity, and clinical validity while preserving patient privacy. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8f5cf824-5a76-4176-b786-81b2b5f8e97c
- author
- Hashemi, Atiye Sadat LU ; Soliman, Amira ; Lundström, Jens and Etminani, Kobra
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Caring is Sharing – Exploiting the Value in Data for Health and Innovation
- series title
- Studies in Health Technology and Informatics
- volume
- 302
- pages
- 352 - 353
- external identifiers
-
- scopus:85159760846
- DOI
- 10.3233/SHTI230136
- language
- English
- LU publication?
- no
- id
- 8f5cf824-5a76-4176-b786-81b2b5f8e97c
- date added to LUP
- 2025-01-31 14:18:07
- date last changed
- 2025-02-04 04:01:22
@inbook{8f5cf824-5a76-4176-b786-81b2b5f8e97c, abstract = {{Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing artificial intelligence algorithms. However, access to "real" healthcare data is a big challenge due to ethical and legal reasons. There is also a need to deal with challenges around electronic health records (EHRs) including biased, heterogeneity, imbalanced data, and small sample sizes. In this study, we introduce a domain knowledge-driven framework for generating synthetic EHRs, as an alternative to methods only using EHR data or expert knowledge. By leveraging external medical knowledge sources in the training algorithm, the suggested framework is designed to maintain data utility, fidelity, and clinical validity while preserving patient privacy.}}, author = {{Hashemi, Atiye Sadat and Soliman, Amira and Lundström, Jens and Etminani, Kobra}}, booktitle = {{Caring is Sharing – Exploiting the Value in Data for Health and Innovation}}, language = {{eng}}, pages = {{352--353}}, series = {{Studies in Health Technology and Informatics}}, title = {{Domain Knowledge-Driven Generation of Synthetic Healthcare Data}}, url = {{http://dx.doi.org/10.3233/SHTI230136}}, doi = {{10.3233/SHTI230136}}, volume = {{302}}, year = {{2023}}, }