Methods for generating and evaluating synthetic longitudinal patient data: a systematic review
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866908657765056512 |
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| author | Perkonoja, Katariina Auranen, Kari Virta, Joni |
| author_facet | Perkonoja, Katariina Auranen, Kari Virta, Joni |
| contents | The rapid growth in data availability has facilitated research and development, yet not all industries have benefited equally due to legal and privacy constraints. The healthcare sector faces significant challenges in utilizing patient data because of concerns about data security and confidentiality. To address this, various privacy-preserving methods, including synthetic data generation, have been proposed. Synthetic data replicate existing data as closely as possible, acting as a proxy for sensitive information. While patient data are often longitudinal, this aspect remains underrepresented in existing reviews of synthetic data generation in healthcare. This paper maps and describes methods for generating and evaluating synthetic longitudinal patient data in real-life settings through a systematic literature review, conducted following the PRISMA guidelines and incorporating data from five databases up to May 2024. Thirty-nine methods were identified, with four addressing all challenges of longitudinal data generation, though none included privacy-preserving mechanisms. Resemblance was evaluated in most studies, utility in the majority, and privacy in just over half. Only a small fraction of studies assessed all three aspects. Our findings highlight the need for further research in this area. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_12380 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Methods for generating and evaluating synthetic longitudinal patient data: a systematic review Perkonoja, Katariina Auranen, Kari Virta, Joni Methodology Cryptography and Security Machine Learning Applications The rapid growth in data availability has facilitated research and development, yet not all industries have benefited equally due to legal and privacy constraints. The healthcare sector faces significant challenges in utilizing patient data because of concerns about data security and confidentiality. To address this, various privacy-preserving methods, including synthetic data generation, have been proposed. Synthetic data replicate existing data as closely as possible, acting as a proxy for sensitive information. While patient data are often longitudinal, this aspect remains underrepresented in existing reviews of synthetic data generation in healthcare. This paper maps and describes methods for generating and evaluating synthetic longitudinal patient data in real-life settings through a systematic literature review, conducted following the PRISMA guidelines and incorporating data from five databases up to May 2024. Thirty-nine methods were identified, with four addressing all challenges of longitudinal data generation, though none included privacy-preserving mechanisms. Resemblance was evaluated in most studies, utility in the majority, and privacy in just over half. Only a small fraction of studies assessed all three aspects. Our findings highlight the need for further research in this area. |
| title | Methods for generating and evaluating synthetic longitudinal patient data: a systematic review |
| topic | Methodology Cryptography and Security Machine Learning Applications |
| url | https://arxiv.org/abs/2309.12380 |