  Electronic Health Records (EHRs) contain a wealth of patient data; however,
the sparsity of EHRs data often presents significant challenges for predictive
modeling. Conventional imputation methods inadequately distinguish between real
and imputed data, leading to potential inaccuracies of patient representations.
To address these issues, we introduce PRISM, a framework that indirectly
imputes data by leveraging prototype representations of similar patients, thus
ensuring compact representations that preserve patient information. PRISM also
includes a feature confidence learner module, which evaluates the reliability
of each feature considering missing statuses. Additionally, PRISM introduces a
new patient similarity metric that accounts for feature confidence, avoiding
overreliance on imprecise imputed values. Our extensive experiments on the
MIMIC-III, MIMIC-IV, PhysioNet Challenge 2012, eICU datasets demonstrate
PRISM's superior performance in predicting in-hospital mortality and 30-day
readmission tasks, showcasing its effectiveness in handling EHR data sparsity.
For the sake of reproducibility and further research, we have made the code
publicly available at https://github.com/yhzhu99/PRISM.
