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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.14721 |
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| _version_ | 1866909968250175488 |
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| author | Appenzeller, Arno Terzer, Nick Homeyer, André Redlich, Jan-Philipp Luttmann, Sabine Feuerhake, Friedrich Schaadt, Nadine S. Intemann, Timm Teuber-Hanselmann, Sarah Nikolin, Stefan Weis, Joachim Kraywinkel, Klaus Birnstill, Pascal |
| author_facet | Appenzeller, Arno Terzer, Nick Homeyer, André Redlich, Jan-Philipp Luttmann, Sabine Feuerhake, Friedrich Schaadt, Nadine S. Intemann, Timm Teuber-Hanselmann, Sarah Nikolin, Stefan Weis, Joachim Kraywinkel, Klaus Birnstill, Pascal |
| contents | The generation of synthetic data is a promising technology to make medical data available for secondary use in a privacy-compliant manner. A popular method for creating realistic patient data is the rule-based Synthea data generator. Synthea generates data based on rules describing the lifetime of a synthetic patient. These rules typically express the probability of a condition occurring, such as a disease, depending on factors like age. Since they only contain statistical information, rules usually have no specific data protection requirements. However, creating meaningful rules can be a very complex process that requires expert knowledge and realistic sample data. In this paper, we introduce and evaluate an approach to automatically generate Synthea rules based on statistics from tabular data, which we extracted from cancer reports. As an example use case, we created a Synthea module for glioblastoma from a real-world dataset and used it to generate a synthetic dataset. Compared to the original dataset, the synthetic data reproduced known disease courses and mostly retained the statistical properties. Overall, synthetic patient data holds great potential for privacy-preserving research. The data can be used to formulate hypotheses and to develop prototypes, but medical interpretation should consider the specific limitations as with any currently available approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Extraction of Rules for Generating Synthetic Patient Data From Real-World Population Data Using Glioblastoma as an Example Appenzeller, Arno Terzer, Nick Homeyer, André Redlich, Jan-Philipp Luttmann, Sabine Feuerhake, Friedrich Schaadt, Nadine S. Intemann, Timm Teuber-Hanselmann, Sarah Nikolin, Stefan Weis, Joachim Kraywinkel, Klaus Birnstill, Pascal Machine Learning I.6; J.3 The generation of synthetic data is a promising technology to make medical data available for secondary use in a privacy-compliant manner. A popular method for creating realistic patient data is the rule-based Synthea data generator. Synthea generates data based on rules describing the lifetime of a synthetic patient. These rules typically express the probability of a condition occurring, such as a disease, depending on factors like age. Since they only contain statistical information, rules usually have no specific data protection requirements. However, creating meaningful rules can be a very complex process that requires expert knowledge and realistic sample data. In this paper, we introduce and evaluate an approach to automatically generate Synthea rules based on statistics from tabular data, which we extracted from cancer reports. As an example use case, we created a Synthea module for glioblastoma from a real-world dataset and used it to generate a synthetic dataset. Compared to the original dataset, the synthetic data reproduced known disease courses and mostly retained the statistical properties. Overall, synthetic patient data holds great potential for privacy-preserving research. The data can be used to formulate hypotheses and to develop prototypes, but medical interpretation should consider the specific limitations as with any currently available approach. |
| title | Automatic Extraction of Rules for Generating Synthetic Patient Data From Real-World Population Data Using Glioblastoma as an Example |
| topic | Machine Learning I.6; J.3 |
| url | https://arxiv.org/abs/2512.14721 |