Clinical Text Generation through Leveraging Medical Concept and Relations

Abstract

With a neural sequence generation model, this study aims to develop a method writing patient clinical texts given brief medical history. As a proof-of-a-concept, we have demonstrated that it can be workable to use medical concept embedding in clinical text generation. Our model was based on the Sequence-to-Sequence architecture and trained with a large set of de-identified clinical text data. The quantitative result shows that our concept embedding method decreased the perplexity of the baseline architecture. Also, we discuss the analyzed results from human evaluation performed by medical doctors.

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