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Many real-world Electronic Health Record (EHR) data contains a largeproportion of missing values. Leaving substantial portion of missinginformation unaddressed usually causes significant bias, which leads to invalidconclusion to be drawn. On the other hand, training a machine learning modelwith a much smaller nearly-complete subset can drastically impact thereliability and accuracy of model inference. Data imputation algorithms thatattempt to replace missing data with meaningful values inevitably increase thevariability of effect estimates with increased missingness, making itunreliable for hypothesis validation. We propose a novel Ensemble-Learning forMissing Value (ELMV) framework, which introduces an effective approach toconstruct multiple subsets of the original EHR data with a much lower missingrate, as well as mobilizing a dedicated support set for the ensemble learningin the purpose of reducing the bias caused by substantial missing values. ELMVhas been evaluated on a real-world healthcare data for critical featureidentification as well as a batch of simulation data with different missingrates for outcome prediction. On both experiments, ELMV clearly outperformsconventional missing value imputation methods and ensemble learning models.

ELMV: an Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing Values