  Electronic health records (EHRs) have become the foundation of machine
learning applications in healthcare, while the utility of real patient records
is often limited by privacy and security concerns. Synthetic EHR generation
provides an additional perspective to compensate for this limitation. Most
existing methods synthesize new records based on real EHR data, without
consideration of different types of events in EHR data, which cannot control
the event combinations in line with medical common sense. In this paper, we
propose MSIC, a Multi-visit health Status Inference model for Collaborative EHR
synthesis to address these limitations. First, we formulate the synthetic EHR
generation process as a probabilistic graphical model and tightly connect
different types of events by modeling the latent health states. Then, we derive
a health state inference method tailored for the multi-visit scenario to
effectively utilize previous records to synthesize current and future records.
Furthermore, we propose to generate medical reports to add textual descriptions
for each medical event, providing broader applications for synthesized EHR
data. For generating different paragraphs in each visit, we incorporate a
multi-generator deliberation framework to collaborate the message passing of
multiple generators and employ a two-phase decoding strategy to generate
high-quality reports. Our extensive experiments on the widely used benchmarks,
MIMIC-III and MIMIC-IV, demonstrate that MSIC advances state-of-the-art results
on the quality of synthetic data while maintaining low privacy risks.
