  Generative models can produce synthetic patient records for analytical tasks
when real data is unavailable or limited. However, current methods struggle
with adhering to domain-specific knowledge and removing invalid data. We
present ConSequence, an effective approach to integrating domain knowledge into
sequential generative neural network outputs. Our rule-based formulation
includes temporal aggregation and antecedent evaluation modules, ensured by an
efficient matrix multiplication formulation, to satisfy hard and soft logical
constraints across time steps. Existing constraint methods often fail to
guarantee constraint satisfaction, lack the ability to handle temporal
constraints, and hinder the learning and computational efficiency of the model.
In contrast, our approach efficiently handles all types of constraints with
guaranteed logical coherence. We demonstrate ConSequence's effectiveness in
generating electronic health records, outperforming competitors in achieving
complete temporal and spatial constraint satisfaction without compromising
runtime performance or generative quality. Specifically, ConSequence
successfully prevents all rule violations while improving the model quality in
reducing its test perplexity by 5% and incurring less than a 13% slowdown in
generation speed compared to an unconstrained model.
