  Electronic health records (EHR) contain a large variety of information on the
clinical history of patients such as vital signs, demographics, diagnostic
codes and imaging data. The enormous potential for discovery in this rich
dataset is hampered by its complexity and heterogeneity.
  We present the first study to assess unsupervised homogenization pipelines
designed for EHR clustering. To identify the optimal pipeline, we tested
accuracy on simulated data with varying amounts of redundancy, heterogeneity,
and missingness. We identified two optimal pipelines: 1) Multiple Imputation by
Chained Equations (MICE) combined with Local Linear Embedding; and 2) MICE,
Z-scoring, and Deep Autoencoders.
