Chang Sun, Dr. (C.)
Key publications
Sun, C., van Soest, J., & Dumontier, M. (2023). Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy. Journal of Biomedical Informatics, 143, Article 104404. https://doi.org/10.1016/j.jbi.2023.104404
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Sun, C., Ocana, M. G., van Soest, J., & Dumontier, M. (2023). ciTIzen-centric DAta pLatform (TIDAL): Sharing distributed personal data in a privacy-preserving manner for health research. Semantic web, 14(5), 977-996. https://doi.org/10.3233/SW-223220
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van Soest, J., Sun, C., Mussmann, O., Puts, M., van den Berg, B., Malic, A., van Oppen, C., Townend, D., Dekker, A., & Dumontier, M. (2018). Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. In BUILDING CONTINENTS OF KNOWLEDGE IN OCEANS OF DATA: THE FUTURE OF CO-CREATED EHEALTH (Vol. 247, pp. 581-585). IOS Press. https://doi.org/10.3233/978-1-61499-852-5-581
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Sun, C., van Soest, J., & Dumontier, M. (2023). Analyze Decentralized Personal Health Data using Solid, Digital Consent, and Federated Learning. In Semantic Web Applications and Tools for Health Care and Life Sciences 2023 (Vol. 3415, pp. 169-170) https://bbuelens.solidcommunity.net/public/html/SolidSymposium2023_ChangSun.pdf
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Eigenschink, P., Reutterer, T., Vamosi, S., Vamosi, R., Sun, C., & Kalcher, K. (2023). Deep Generative Models for Synthetic Data: A Survey. IEEE Access, 11, 47304-47320. https://doi.org/10.1109/ACCESS.2023.3275134
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Sun, C., van Soest, J., Koster, A., Eussen, S. J. P. M., Schram, M. T., Stehouwer, C. D. A., Dagnelie, P. C., & Dumontier, M. (2022). Studying the association of diabetes and healthcare cost on distributed data from the Maastricht Study and Statistics Netherlands using a privacy-preserving federated learning infrastructure. Journal of Biomedical Informatics, 134, Article 104194. https://doi.org/10.1016/j.jbi.2022.104194
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Sun, C., Emonet, V., & Dumontier, M. (2022). A comprehensive comparison of automated FAIRness Evaluation Tools. In Semantic Web Applications and Tools for Health Care and Life Sciences (Vol. 3127, pp. 44-53)
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Sun, C., Emonet, V., van Soest, J., Koster, A., Dekker, A., & Dumontier, M. (2019). Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure. In Semantic Web Applications and Tools for Health Care and Life Sciences (Vol. 2849, pp. 141-142) https://ceur-ws.org/Vol-2849/paper-21.pdf
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Urovi, V., Celebi, R., Sun, C., Rieswijk, L., Erard, M., Yilmaz, A., Moodley, K., Kumar, P., & Dumontier, M. (2023). TAPS Responsibility Matrix: A tool for responsible data science by design. (pp. 2302.01041v1). https://doi.org/10.48550/arXiv.2302.01041
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Sijstermans, R., Sun, C., & Hortal, E. (2024). Mitigating Class Imbalance in Time Series with Enhanced Diffusion Models. In J. M. Ferrández Vicente, M. Val Calvo, & H. Adeli (Eds.), Bioinspired Systems for Translational Applications: From Robotics to Social Engineering: 10th International Work-Conference on the Interplay Between Natural and Artificial Computation (pp. 389-399). Springer Nature. https://doi.org/10.1007/978-3-031-61137-7_36
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