Clinical Text Generation through Leveraging Medical Concept and Relations

With a neural sequence generation model, this study aims to develop a method of writing the patient clinical text s given a brief medical history. As a proof - of - a - concept, we have demonstrated that it can be workable t o 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 . T he quantitative result shows that our concept embedding method decr eased the perplexity of the baseline architecture . Also, we discuss the analyzed r esults from a human evaluation performed by medical doctors .