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

SUBMITTER: Miletic M 

PROVIDER: S-EPMC12849454 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature