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Background <p>High-quality healthcare data is often hard to access due to privacy rules and limited availability, making it difficult to develop and test clinical AI models.</p> Objective <p>We propose a Deep Generative Hidden Markov Model (DG-HMM) that combines deep neural networks with probabilistic sequential modeling to generate synthetic patient data that is clinically realistic while protecting privacy.</p> Methods <p>DG-HMM uses a deep encoder-decoder structure with a flexible HMM layer to model temporal patterns and mixed clinical features. We trained and tested it on three real datasets: MIMIC-III ICU records, long-term diabetes data, and mental health progression records. We measured statistical similarity, temporal consistency, clinical rule compliance, and privacy protection.</p> Results <p>DG-HMM demonstrated superior performance compared to baseline methods, preserving about 94.2% of correlations, following clinical rules in 96.3% of cases, and resisting membership inference attacks in 89.4% of attempts. Predictive models trained on synthetic data were only 2–5% less accurate than those trained on real data.</p> Conclusion <p>DG-HMM offers a robust framework to create synthetic healthcare data for research and collaboration where privacy limits real data sharing. It can help support medical AI development in constrained settings.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Mohammadreza Momenzadeh,
  • Atiyeh Oshaghi

摘要

Background

High-quality healthcare data is often hard to access due to privacy rules and limited availability, making it difficult to develop and test clinical AI models.

Objective

We propose a Deep Generative Hidden Markov Model (DG-HMM) that combines deep neural networks with probabilistic sequential modeling to generate synthetic patient data that is clinically realistic while protecting privacy.

Methods

DG-HMM uses a deep encoder-decoder structure with a flexible HMM layer to model temporal patterns and mixed clinical features. We trained and tested it on three real datasets: MIMIC-III ICU records, long-term diabetes data, and mental health progression records. We measured statistical similarity, temporal consistency, clinical rule compliance, and privacy protection.

Results

DG-HMM demonstrated superior performance compared to baseline methods, preserving about 94.2% of correlations, following clinical rules in 96.3% of cases, and resisting membership inference attacks in 89.4% of attempts. Predictive models trained on synthetic data were only 2–5% less accurate than those trained on real data.

Conclusion

DG-HMM offers a robust framework to create synthetic healthcare data for research and collaboration where privacy limits real data sharing. It can help support medical AI development in constrained settings.