Multi-Resolution Generation of Electronic Health Records

Generative models for electronic health records aim to synthesize plausible future clinical trajectories conditioned on a patient’s prior history. These approaches support a range of downstream applications, including risk stratification and phenotype discovery. However, most existing methods generate trajectories at a predetermined resolution - on the order of hours or days - making long-horizon forecasting computational expensive and error-prone. The few approaches that incorporate multiple temporal resolutions scale poorly to multiple outcomes of interest and require costly training procedures. Thus, we present Multiple Resolution Generation of Electronic Health Records (MuRGEHR – pronounced merger), a framework for the efficient generation at multiple resolution over long horizons without sacrificing fidelity. MuRGEHR models the EHR time series as a hierarchical sequence of tokens representing different temporal resolutions. The model first constructs a coarse scaffold, which then serves as conditioning for synthesizing progressively finer resolutions. Across three datasets and multiple prediction horizons, this hierarchical strategy yields consistent gains in computational efficiency while remaining competitive with regards to fidelity on downstream clinical tasks.