  An applied problem facing all areas of data science is harmonizing data
sources. Joining data from multiple origins with unmapped and only partially
overlapping features is a prerequisite to developing and testing robust,
generalizable algorithms, especially in health care. We approach this issue in
the common but difficult case of numeric features such as nearly Gaussian and
binary features, where unit changes and variable shift make simple matching of
univariate summaries unsuccessful. We develop two novel procedures to address
this problem. First, we demonstrate multiple methods of "fingerprinting" a
feature based on its associations to other features. In the setting of even
modest prior information, this allows most shared features to be accurately
identified. Second, we demonstrate a deep learning algorithm for translation
between databases. Unlike prior approaches, our algorithm takes advantage of
discovered mappings while identifying surrogates for unshared features and
learning transformations. In synthetic and real-world experiments using two
electronic health record databases, our algorithms outperform existing
baselines for matching variable sets, while jointly learning to impute unshared
or transformed variables.
