MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS
2016; Springer Nature; Linguagem: Inglês
10.1142/9789813207813_0021
ISSN1571-4861
AutoresBrett K. Beaulieu‐Jones, Jason H. Moore,
Tópico(s)Genetic Associations and Epidemiology
ResumoBiocomputing 2017, pp. 207-218 (2017) Open AccessMISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERSBRETT K. BEAULIEU-JONES, JASON H. MOORE, and THE POOLED RESOURCE OPEN-ACCESS ALS CLINICAL TRIALS CONSORTIUMBRETT K. BEAULIEU-JONESGenomics 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, JASON H. MOOREComputational Genetics Lab, Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia PA, 19104, USA, and THE POOLED RESOURCE OPEN-ACCESS ALS CLINICAL TRIALS CONSORTIUMhttps://doi.org/10.1142/9789813207813_0021Cited by:37 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail 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. This work is supported by a Commonwealth Universal Research Enhancement (CURE) Program grant from the Pennsylvania Department of Health. 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