Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction

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 …

clinical cs.lg data ehr electronic electronic health records health imputation medical missing values networks neural networks patient patients prediction prompt records research values