ELMV: an Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing Values
This addresses the issue of missing data in EHRs for healthcare researchers and practitioners, but it is incremental as it builds on existing ensemble learning approaches.
The paper tackles the problem of significant missing values in Electronic Health Record (EHR) data, which can cause bias and reduce model reliability, by proposing the ELMV framework that constructs multiple subsets with lower missing rates and uses ensemble learning, resulting in clear outperformance over conventional imputation and ensemble methods in experiments on real-world and simulation data.
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.