  A variety of methods existing for generating synthetic electronic health
records (EHRs), but they are not capable of generating unstructured text, like
emergency department (ED) chief complaints, history of present illness or
progress notes. Here, we use the encoder-decoder model, a deep learning
algorithm that features in many contemporary machine translation systems, to
generate synthetic chief complaints from discrete variables in EHRs, like age
group, gender, and discharge diagnosis. After being trained end-to-end on
authentic records, the model can generate realistic chief complaint text that
preserves much of the epidemiological information in the original data. As a
side effect of the model's optimization goal, these synthetic chief complaints
are also free of relatively uncommon abbreviation and misspellings, and they
include none of the personally-identifiable information (PII) that was in the
training data, suggesting it may be used to support the de-identification of
text in EHRs. When combined with algorithms like generative adversarial
networks (GANs), our model could be used to generate fully-synthetic EHRs,
facilitating data sharing between healthcare providers and researchers and
improving our ability to develop machine learning methods tailored to the
information in healthcare data.
