Synthetic data generation methods for longitudinal and time series health data: a systematic review
Abstract
Background: Synthetic data generation (SDG) has emerged as a critical enabler for data-driven healthcare research, offering privacy-preserving alternatives to real patient data. Temporal health data - ranging from physiological signals to electronic health records (EHRs) - pose unique challenges for SDG due to their complexity, irregularity, and clinical sensitivity.
Objective: This review systematically examines SDG methods for longitudinal and time-series health data. Its aims are to (1) propose a lightweight taxonomy to support orientation across the SDG landscape along five structural dimensions, (2) characterize the major synthesis techniques and their alignment with temporal structures and data modalities, and (3) synthesize the utility and privacy evaluation strategies used in practice.
Methods: A systematic literature review was conducted following PRISMA guidelines across four major databases (ACM, arXiv, IEEE Xplore, Europe PMC) for publications from 2017 to 2025. Eligible studies proposed or applied SDG techniques to healthcare-relevant temporal data with sufficient methodological transparency. Structured data extraction and thematic analysis were used to identify modeling trends, evaluation metrics, and domain-specific requirements, complemented by a comparative synthesis of SDG methods.
Results: A total of 115 studies were included. Deep generative models - especially Generative Adversarial Networks (GANs), Autoencoders (AEs), and diffusion-based methods -…
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