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

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. 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. 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. 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. 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.