EXITs: An Ensemble Approach for Imputing Missing EHR Data

Abstract

Missing data points are prevalent in electronic health records (EHRs) and are an impedance to utilizing machine learning for predictive and classification tasks in healthcare. For this challenge, we developed eXITs-A stacked ensemble learner that employs 6 base models to perform imputation on time series data from 13 different laboratory tests across 8, 267 patients in the MIMIC-III database provided in the ICHI 2019 Data Analytics Challenge on Missing Data Imputation (DACMI). The results show that our ensemble model (avg. nRMSE = 0.200) outperforms the reference model, 3D-MICE (avg. nRMSE = 0.222) by 9.69%.