Synthetic data generation for tabular health records: A systematic review

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322

The potential for artificial intelligence in healthcare

Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800

Five steps to conducting a systematic review

Khan, K. S., Kunz, R., Kleijnen, J., & Antes, G. (2003) · Journal of the Royal Society of Medicine · doi:10.1177/014107680309600304 · Cited by 1,873

Privacy-preserving data publishing: A survey of recent developments

Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704

Privacy in the age of medical big data

Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522

Data synthesis based on generative adversarial networks

Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., & Kim, Y. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3231751.3231757 · Cited by 567

Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record

Walonoski, J. A., Kramer, M. A., Nichols, J., Quina, A., Moesel, C., Hall, D., Duffett, C., Dube, K., Gallagher, T., & McLachlan, S. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx079 · Cited by 521

A General Survey of Privacy-Preserving Data Mining Models and Algorithms

Aggarwal, C. C., & Yu, P. S. (2008) · The Kluwer international series on advances in database systems · doi:10.1007/978-0-387-70992-5_2 · Cited by 481

Applications of Generative Adversarial Networks (GANs): An Updated Review

Alqahtani, H., Kavakli, M., & Kumar, G. (2019) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-019-09388-y · Cited by 457

Generation and evaluation of synthetic patient data

Gonçalves, A. R., Ray, P., Soper, B., Stevens, J. L., Coyle, L. M., & Sales, A. P. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00977-1 · Cited by 456

Improving Stress Detection with Synthetic Datasets: GPT-4o-Mini and Transformer Model Evaluation

Badran, N., Le, J., Le, T., & Uchiya, T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6008-7_9

Limitations of Generative AI in Real-Time Decision-Making

Chand, C. (2025) · doi:10.38124/ijisrt/25jun935

Latent Diffusion Models with Correlation Loss for Tabular Data Generation

Zhang, Q. (2025) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice64954.2025.11064643

Propensity-score matching with GAN-generated observations from electronic health records: simulation study and application to the evaluation of prone positioning in COVID-19 patients under mechanical ventilationPreprint

Bouvarel, B., Glémain, B., Carrat, F., & Lapidus, N. (2025) · medRxiv · doi:10.1101/2025.07.31.25332504

Synthetic Data Usage for Healthcare Privacy Using GENERATIVE AI

Sahu, P. R., Tripathy, A., & Tripathy, A. R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-90723-4_35

Evaluating Fidelity and Machine Learning Utility of Synthetic Tabular Data Generated Using Generative ModelsPreprint

Dhaka, A. K., Roy, A., & Shrivallabha, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7287372/v1

Towards Next-Generation Digital Twins for Diabetes

Gudivaka, R. L., Grandhi, S. H., Gudivaka, B. R., Gudivaka, R. K., Basani, D. K. R., & Sarvamangala, D. R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7062-0.ch009

Probabilistic vs Deep Generative Models: A Fairness Centred Evaluation of Synthetic Healthcare Tabular DataPreprint

Alattal, D. R., Draghi, B., Myles, P. R., Branson, R. D., & Tucker, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7565139/v1

Designing an Evaluation Chain for Assessing the Quality of Data Generation by Tabular GANs

Campanile, L., Marrone, S., Marulli, F., & Verde, L. (2024) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3649601.3698755

Convex space learning for tabular synthetic data generation

Mahendra, M., Umesh, C., Schultz, K., Olaf Wolkenhauer, A., & Bej, S. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131722

AndroGen: Open-source synthetic data generation for automated sperm analysis

Hernández, D. L., Pantrigo, J. J., Montalvo, S., & Cabido, R. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109132

Synthetic Anatomy: Deep Learning Models for Virtual Population Generation–A ReviewPreprint

Zhou, Z., Zakeri, A., Dou, H., Xue, Y., MacRaild, M., Huang, J., Lin, F., Sarrami‐Foroushani, A., Duan, J., & Frangi, A. F. (2025) · medRxiv · doi:10.1101/2025.10.28.25338782 · Cited by 1

Illustrating a Monthly Professional Newsletter With Synthetic Visuals

Hai-Jew, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2277-3.ch012

SDSP: Scalable and Diverse Synthetic Pairwise Text Generation from Web Corpus Using Large Language Model

Wu, X., Li, X., Wu, W., Timofeev, A., Yang, Y., Cao, M., Huang, P., Li, S., & Shan, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4367-0_1

The Role of Domain-Specific Models for Synthetic Data Generation with Iterative Prompt Optimization

Freise, N., Heitlinger, M., Nuredini, R., & Meixner, G. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252935

Energy-efficient meta-classifier model for log access anomaly detection in healthcare systems

Matos, M., Gomes, F., Nogueira, F., & Almeida, F. L. (2025) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-08-2025-0548

Why do we need high-fidelity synthetic eye movement data and how should they look like?Preprint

Qian, C. S., Aziz, S., Hasan, K., & Komogortsev, O. V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.11.692112

Additional file 5 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26737947

Additional file 3 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26737941

Additional file 7 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26737953

Additional file 6 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26737950

Additional file 2 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26085211

Additional file 1 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26085113

Additional file 4 of Learning debiased graph representations from the OMOP common data model for synthetic data generation

Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., & Gundler, C. (2024) · Figshare · doi:10.6084/m9.figshare.26737944

Bridging Data Limitations in Neurosurgery with Synthetic Data Generation: Generative Adversarial Network-Powered Multiclass Classification of Aneurysm Occlusion Grades

Babel, S., Pressman, E., Weinberg, J. H., Baig, A. A., Rainone, G. J., Schimmel, S., Vignolles-Jeong, J., Smith, T., Youssef, P., Davies, J. M., et al. (2025) · AI in neuroscience. · doi:10.1177/2997979x251369456

Synthetic Data for Infertility Research: A Literature Review

Szatmáry, R., Szatmáry, K. S., & Wang, F.-Y. (2025) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti67731.2025.11311699

Sint Multiverse: Prototype Simulator for Generating Synthetic Health Determinant Data for Explainable AI Training

Jung, M. O., Pintanel, A. T. G., Bärwaldt, R., Santos, H., & Dimuro, G. P. (2026) · IEEE Access · doi:10.1109/access.2026.3651970

A Methodology for the Generation and Evaluation of Tabular Synthetic Data: A Case Study in Data Analysis in Intensive Care Units

Bastida, M. D., Vázquez, R. A. P., & Pérez, R. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-11358-0_30

Generative Synthesis of Precipitation Radar Data for Disaster IT Testbed

Hiroi, K., Kohiga, A., Fukaya, S., & Shinoda, Y. (2026) · Journal of Information Processing · doi:10.2197/ipsjjip.34.161

Vision Transformers-Enhanced Signal Denoising System for Robust 6G Communication

Agrawal, A. V., Girish, P., Gomathy, S., Kistan, A., Sashmi, S. N., & Dhanraj, J. (2025) · International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) · doi:10.1109/iceconf65644.2025.11379533

Synthetic Data Review Using Generative Adversarial Networks and Transformer Models in AQ-10 Autism Spectrum Disorder Dataset

Solagratia, N. P., & Thejas, G. S. (2025) · International Conference on Artificial Intelligence and Technological Solutions (ICAITech) · doi:10.1109/icaitech66481.2025.11387099

Deep generative hidden Markov models for synthetic patient data generation: a novel approach for medical AI research

Momenzadeh, M., & Oshaghi, A. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03396-2

Exploring the Efficacy of Large Language Models for Synthetic Data Generation in Laser-Accelerated Ion Beam Studies

Menne, F., Zöller, T., & Schaaff, K. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391024

Non-destructive chick embryo mortality prediction at pre-incubation and early incubation using hyperspectral imaging and explainable artificial intelligence

Ahmed, M. W., Emmert, J. L., & Kamruzzaman, M. (2026) · British Poultry Science · doi:10.1080/00071668.2026.2620615

Probabilistic Versus Deep Generative Models: A Fairness Centred Evaluation of Synthetic Healthcare Tabular Data

Alattal, D. R., Draghi, B., Myles, P. R., Branson, R., & Tucker, A. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01173-7

Diffusion Models for Synthetic Tabular Data Generation

Telesheva, E., & Hushchyn, M. (2025) · Doklady Mathematics · doi:10.1134/s1064562425700553

A privacy-preserving federated clustering algorithm for data imbalance based on density peak clustering and gaussian distribution simulation data

Wang, J., & Chen, X. (2026) · Cluster Computing · doi:10.1007/s10586-026-06013-z

Synthetic tabular health data generation: a practical comparison between correlation and model-based statistical approach and the conditional generative adversarial network approach

Zhang, Y., & Muller, S. (2026) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2026.2653039

Addressing Gender Data Gaps in the Global Majority: Opportunities and Challenges of Synthetic Data

Musizvingoza, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-07830-8_25

Assessment of differentially private fine-tuning of large language models for synthetic clinical note generation

Sarkar, A. R., Sarmin, F. J., Mouheb, D., Fung, B. C. M., & Mohammed, N. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200659

A Data Deduplication Pipeline for Indonesian Athlete Registry: A Synthetic Data Approach

Widi, A., Sediyono, E., & Hendry, H. (2026) · International Seminar on Intelligent Business and Edge-Computing Research (ISIBER) · doi:10.1109/isiber68248.2026.11469995

Quantifying Privacy Risks in Synthetic Data: A Study on Black-Box Membership Inference

Fantino, G., Rondina, M., Vetrò, A., & Martin, J. C. D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22774-4_5

Tabular Synthesis Based on Bi-Directional Feedback Conditional Diffusion Models

Zhang, Q., Tang, Y., Tian, J., He, Y., Xu, L., & Liu, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464381

Validating Synthetic Data for Behavioral Modeling

Marchev, A., Marchev, V., & Efremov, A. (2025) · International Conference on Algorithms, Computing and Systems (ICACS) · doi:10.1145/3789418.3789435

Multi-Task TabGraphSyn: Graph-Based Synthetic EHR Generation with Improved Quality-Privacy Trade-offs for Opioid Use Disorder PredictionPreprint

Alam, M. A. U., & Shalhout, S. Z. (2026) · medRxiv · doi:10.64898/2026.04.24.26351704

Peeling the Layers of Privacy-Utility Onion on Tabular Data

Wang, J., Zhao, Z., Aniello, L., & Wu, H. (2026) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3805621.3807630

Evaluating model generalizability for suicide attempt risk prediction: traditional machine vs deep learning

Josselyn, N., Sawant, S., Davis-Martin, R. E., Rundensteiner, E. A., Gerber, B. S., Wang, B., Rothschild, A. J., Agu, E., Boudreaux, E. D., & Liu, F.-F. (2026) · npj Mental Health Research · doi:10.1038/s44184-026-00209-2

Privacy-Aware Synthetic Tabular Data Generation for Healthcare: Application to Sepsis Detection

Macias-Fassio, E., Morales, A., Pruenza, C., Fiérrez, J., & Espósito, C. (2026) · Bioengineering · doi:10.3390/bioengineering13050511

Bias-Aware Synthetic Data Generation: A Tailored Use Case-Driven Approach

Draghi, B., Myles, P. R., & Tucker, A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-25308-8_9

Stances toward data governance: Negotiating tensions in data sharing for artificial intelligence in healthcare

Gubser, R., Schulte‐Althoff, M., Pohle, J., Heinemann, N., Balzer, F., & Fürstenau, D. (2026) · Information and Organization · doi:10.1016/j.infoandorg.2026.100625

Adaptive Generative AI Framework for Creating Rare-Disease Synthetic EHRs with Built-In Bias Mitigation and Privacy Guarantees

Jewel, R. M., Bhuiyan, M. S., Rahman, M. H., Linkon, A. A., Pervin, T., & Anjum, N. (2026) · Westcliff International Journal of Applied Research · doi:10.47670/wuwijar20261rmmatn

AI agents: a game changer in ophthalmology?

Ramesh, D., Tao, B. K.-L., & Hutnik, C. M. L. (2026) · Eye · doi:10.1038/s41433-026-04543-9

Synthetic RNA-seq cohorts for data sharing: a discovery-aware benchmark at transcriptome scalePreprint

Nanda, A., & Saha, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.22.726357

A Machine Learning Framework for Autonomous Bilge Water Management

Dutt, S., & Kanungo, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18132-9_5

Synthetic Data Generation for Healthcare and Wellness: Methods, Applications, and Future Directions

Subaramaniam, K., Palaniapan, S., Baker, O. F., Bui, N. D., & Sumit, D. (2026) · Journal of Informatics and Web Engineering · doi:10.33093/jiwe.2026.5.2.21

An integrated evaluation framework for synthetic clinical data in severely imbalanced settings: fidelity, privacy-risk profiling, and diagnostic utility

Kim, Y., Lee, J., Koh, S. B., & Lee, K. M. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03648-1

Evaluation of temporal preservation in synthetic longitudinal patient data

Perkonoja, K., Movahedi, P., Airola, A., Auranen, K. J., & Virta, J. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105074

Prevalence-Aware Machine Learning for Risk Assessment of Rare Medical Conditions

Nawaz, F., Mughal, H., & Fatima, A. (2026) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet69771.2026.11591774

From Noise to Order: Learning to Rank via Denoising Diffusion

Ebrahimi, S., Mitra, B., Arabzadeh, N., Yuan, Y., Wu, H., Zarrinkalam, F., & Ebrahim Bagheri, A. (2026) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3805713.3820437

A Differentially Private Conditional Denoising Autoencoder for Data Augmentation in IgA Nephropathy

Tong, J., Li, J., & Zhu, C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3495-0_17

Quality assessment of synthetic data in healthcare: a critical appraisal with a focus on subgroup bias

Chatzichristos, C., Katsimpokis, D., Lai, C., van Santvliet, L., Camarrone, F., Knoors, D., Geleijnse, G., Speybroek, M. V., Lewi, M., Vannieuwenhuyse, B., et al. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03719-3 · Cited by 1

Hybrid statistical-ensemble machine learning framework for machinability assessment of alloy 800 using chevron shape textured tools

Chhotaray, P., Behera, B. C., & Debnath, K. (2026) · Next research. · doi:10.1016/j.nexres.2026.102262

An Empirical Study of Synthetic Data Generators for Sequences

Akindele, S., Suk, Y., & Yang, K. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817521

LCATS: LLM-Guided Constraint-Aware Tabular Data Synthesis

Li, Q., Shen, Y., Zheng, Q., Liu, Y., & Diao, X. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818181

TabMAGD: Combining Masked Autoencoders with Guided Diffusion for Mixed-Type Tabular Data Synthesis

Qian, Y., Jing, Y., He, Z., Zhang, K., Wang, X. S., Chen, X., & Li, J. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020105

Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation frameworkPreprint

Tran, T., Ghaemi, M. S., & Korosec, C. S. (2026) · medRxiv · doi:10.64898/2026.08.31.26361878

Characterizing Fundamental Differences Between Tabular Synthetic Data Generation Methods

Kroes, S. K. S., Groenwold, R. H. H., Janssen, M. P., & van Leeuwen, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37883-5_17

AI-Generated Medical Datasets for Research and Innovation

Mokashi, B., Valamannavar, S. P., Kumar, M. M., R, S. S., & Kumatgi, S. (2026) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt69916.2026.11688336

Evaluation of synthetic data generation for longitudinal 6-year follow-up subclinical cardiovascular disease data using GAN modelsPreprint

Bertgren, A., Öhberg, F., Soda, P., Näslund, U., Wennberg, P., & Grönlund, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.01.755916

Synthetic Data Generation and Multi-Dimensional Evaluation in Fondazione Italiana Linfomi (FIL) Diffuse Large B-Cell Lymphoma Clinical Cohort

Costa, A., Nera, S., Merli, F., Arcari, A., Cencini, E., Ferrero, S., Contaldo, S. G., Beccuti, M., Cordero, F., Terenziani, A., et al. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00254

Impact of Hyperparameter and Model Selection Optimisation in Synthetic Tabular Data Generation Models in the Healthcare Domain

Catalina, M., Mikel, H., Fernández, M., Nappa, A., Martín, C., Bamidis, P., & Konstantinidis, E. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00033

A synthetic-data and machine-learning digital shadow foundation for end-of-life magnet recycling

Sifakis, N., Chachalis, A., Tsinarakis, G., Aryblia, M., Cholidis, D., & Arampatzis, G. (2026) · Measurement Digitalization · doi:10.1016/j.meadig.2026.100065

A systematic review of synthetic event data generation: overview, challenges, and research directions

Kuhn, M., Zisgen, Y., Grüger, J., Maldonado, A., Koschmider, A., & Bergmann, R. (2026) · Process Science · doi:10.1007/s44311-026-00060-w

A data augmentation-based deep reinforcement learning approach for high-risk states avoidance in sepsis treatment

Zhang, Q., Lin, B., & Liu, Y. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07393-w

CDDM: Constraint-Driven Diffusion Modeling for Tabular DataSynthesisPreprint

Merabet, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7620346

Resolving the Data Dilemma in Catalyst Design: A Generative-Enhanced Manifold-Preserving Machine Learning Method for CO2-to-Aromatics

Wang, Z., Guan, J., Zhou, M., Hu, J., Guo, Q., Wang, M., & Yang, Q. (2026) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.6c03879

Synthetic Data Generation for Multimodal Healthcare Diagnostics: A Systematic Review and Narrative Synthesis Protocol of Image and Tabular Data Engineering (Preprint)Preprint

Komuhangi, G. (2026) · doi:10.2196/preprints.114846

Personal data anonymization in the context of artificial intelligence: from de-Identification to anonymization

Kostrov, S. А., & Потапов, М. П. (2026) · Patient-Oriented Medicine and Pharmacy · doi:10.37489/2949-1924-0147

Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks

Nik, A., Riegler, M. A., Halvorsen, P., & Storås, A. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27077-2_34 · Cited by 17

The Generation of Synthetic Healthcare Data Using Deep Neural Networks

Hossein Zadeh Nik, A. (2022) · NORA - Norwegian Open Research Archives

GANs for Tabular Healthcare Data Generation: A Review on Utility and Privacy

Coutinho‐Almeida, J., Rodrigues, P. P., & Cruz-Correia, R. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88942-5_22 · Cited by 17

A systematic review of privacy-preserving techniques for synthetic tabular health data

Hyrup, T., Lautrup, A. D., Zimek, A., & Schneider–Kamp, P. (2025) · Discover Data · doi:10.1007/s44248-025-00022-w · Cited by 20

Synthetic tabular health data generation: a practical comparison between correlation and model-based statistical approach and the conditional generative adversarial network approach

Zhang, Y., & Muller, S. (2026) · Figshare · doi:10.6084/m9.figshare.31926649