Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of
  Traditional EHR Data Imputation in Downstream Clinical Prediction
Weibin Liao Yinghao Zhu Zixiang Wang Xu Chu Yasha Wang Liantao Ma
Analyzing the health status of patients based on Electronic Health Records
(EHR) is a fundamental research problem in medical informatics. The presence of
extensive missing values in EHR makes it challenging for deep neural networks
to directly model the patient's health status based on EHR. Existing deep
learning training protocols require the use of statistical information or
imputation models to reconstruct missing values; however, the protocols inject
non-realistic data into downstream EHR analysis models, significantly limiting
model performance. This paper introduces Learnable Prompt as Pseudo Imputation
(PAI) as a new training protocol. PAI no longer introduces any imputed data but
constructs a learnable prompt to model the implicit preferences of the
downstream model for missing values, resulting in a significant performance
improvement for all EHR analysis models. Additionally, our experiments show
that PAI exhibits higher robustness in situations of data insufficiency and
high missing rates. More importantly, in a real-world application involving
cross-institutional data with zero-shot evaluation, PAI demonstrates stronger
model generalization capabilities for non-overlapping features.