Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is
challenging due to privacy concerns, which hinders the use of ML for healthcare
applications. Synthetic EHRs generation bypasses the need to share sensitive
real patient records. However, existing methods generate single-modal EHRs by
unconditional generation or by longitudinal inference, which falls short of low
flexibility and makes unrealistic EHRs. In this work, we propose to formulate
EHRs generation as a text-to-text translation task by language models (LMs),
which suffices to highly flexible event imputation during generation. We also
design prompt learning to control the generation conditioned by numerical and
categorical demographic features. We evaluate synthetic EHRs quality by two
perplexity measures accounting for their longitudinal pattern (longitudinal
imputation perplexity, lpl) and the connections cross modalities
(cross-modality imputation perplexity, mpl). Moreover, we utilize two
adversaries: membership and attribute inference attacks for privacy-preserving
evaluation. Experiments on MIMIC-III data demonstrate the superiority of our
methods on realistic EHRs generation (53.1\% decrease of lpl and 45.3\%
decrease of mpl on average compared to the best baselines) with low privacy
risks. Software is available at this https URL.

本文提出了一种基于语言模型将电子医疗记录 (EHRs) 的生成形式转化为文本翻译任务的方法，使得事件插入更加灵活。该方法实现了根据数值和分类人口特征来控制生成的提示学习方式，其评估了两个迷惑度度量，考虑了纵向和跨模态插值的关系，同时利用了隐私保护评估的两个对手，展现了其在 MIMIC-III 数据上生成现实 EHRs 的优越性 (平均 longitudinal imputation perplexity 降低 53.1% 和平均 cross-modality imputation perplexity 降低 45.3% 与最佳基线相比)。