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Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) ischallenging due to privacy concerns, which hinders the use of ML for healthcareapplications. Synthetic EHRs generation bypasses the need to share sensitivereal patient records. However, existing methods generate single-modal EHRs byunconditional generation or by longitudinal inference, which falls short of lowflexibility and makes unrealistic EHRs. In this work, we propose to formulateEHRs generation as a text-to-text translation task by language models (LMs),which suffices to highly flexible event imputation during generation. We alsodesign prompt learning to control the generation conditioned by numerical andcategorical demographic features. We evaluate synthetic EHRs quality by twoperplexity measures accounting for their longitudinal pattern (longitudinalimputation perplexity, lpl) and the connections cross modalities(cross-modality imputation perplexity, mpl). Moreover, we utilize twoadversaries: membership and attribute inference attacks for privacy-preservingevaluation. Experiments on MIMIC-III data demonstrate the superiority of ourmethods on realistic EHRs generation (53.1\% decrease of lpl and 45.3\%decrease of mpl on average compared to the best baselines) with low privacyrisks. Software is available at https://github.com/RyanWangZf/PromptEHR.

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning