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Domain Knowledge-Driven Generation of Synthetic Healthcare Data

Hashemi, Atiye Sadat LU ; Soliman, Amira ; Lundström, Jens and Etminani, Kobra (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:
author
; ; and
publishing date
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}},
}