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Natural language generation for electronic health records
2018
npj Digital Medicine
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
doi:10.1038/s41746-018-0070-0
fatcat:sirstvhjrfexppaks4s2mv5bou