title: MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS.
abstract: Electronic health records (EHRs) have become a vital source of patient outcome data but the widespread prevalence of missing data presents a major challenge. Different causes of missing data in the EHR data may introduce unintentional bias. Here, we compare the effectiveness of popular multiple imputation strategies with a deeply learned autoencoder using the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT). To evaluate performance, we examined imputation accuracy for known values simulated to be either missing completely at random or missing not at random. We also compared ALS disease progression prediction across different imputation models. Autoencoders showed strong performance for imputation accuracy and contributed to the strongest disease progression predictor. Finally, we show that despite clinical heterogeneity, ALS disease progression appears homogenous with time from onset being the most important predictor.
authors.affiliation: Genomics and Computational Biology Graduate Group, Computational Genetics Lab, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia PA, 19104, USA, brettbe@med.upenn.edu.
authors.forename: Brett K
authors.initials: BK
authors.lastname: Beaulieu-Jones
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D000690:Amyotrophic Lateral Sclerosis / Q000503:physiopathology; D015982:Bias; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D019295:Computational Biology; D016208:Databases, Factual / Q000706:statistics & numerical data; D018450:Disease Progression; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D016571:Neural Networks, Computer