  Many real-world Electronic Health Record (EHR) data contains a large
proportion of missing values. Leaving substantial portion of missing
information unaddressed usually causes significant bias, which leads to invalid
conclusion to be drawn. On the other hand, training a machine learning model
with a much smaller nearly-complete subset can drastically impact the
reliability and accuracy of model inference. Data imputation algorithms that
attempt to replace missing data with meaningful values inevitably increase the
variability of effect estimates with increased missingness, making it
unreliable for hypothesis validation. We propose a novel Ensemble-Learning for
Missing Value (ELMV) framework, which introduces an effective approach to
construct multiple subsets of the original EHR data with a much lower missing
rate, as well as mobilizing a dedicated support set for the ensemble learning
in the purpose of reducing the bias caused by substantial missing values. ELMV
has been evaluated on a real-world healthcare data for critical feature
identification as well as a batch of simulation data with different missing
rates for outcome prediction. On both experiments, ELMV clearly outperforms
conventional missing value imputation methods and ensemble learning models.
