Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction. (arXiv:2401.16796v1 [cs.LG])

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 …

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