PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning

Zifeng Wang, Jimeng Sun


Abstract
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 https://github.com/RyanWangZf/PromptEHR.
Anthology ID:
2022.emnlp-main.185
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2873–2885
Language:
URL:
https://aclanthology.org/2022.emnlp-main.185
DOI:
Bibkey:
Cite (ACL):
Zifeng Wang and Jimeng Sun. 2022. PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2873–2885, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning (Wang & Sun, EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.185.pdf