Missing Data Imputation in the Electronic Health Record Using Deeply Learned Autoencoders

@article{BeaulieuJones2017MissingDI,
  title={Missing Data Imputation in the Electronic Health Record Using Deeply Learned Autoencoders},
  author={Brett K. Beaulieu-Jones and Jason H. Moore and et al.},
  journal={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
  year={2017},
  volume={22},
  pages={
          207-218
        }
}
Electronic health records (EHRs) have become a vital source of patient outcome data but the widespread prevalence of missing data presents a major challenge. [...] Key Method 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…Expand Abstract

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