Clinical Text Generation through Leveraging Medical Concept and Relations
This work addresses the problem of automating clinical text generation for healthcare professionals, but it is incremental as it builds on existing Sequence-to-Sequence methods with concept embeddings.
The study tackled generating patient clinical texts from brief medical histories using a neural sequence generation model, resulting in decreased perplexity compared to a baseline architecture.
With a neural sequence generation model, this study aims to develop a method of writing the patient clinical texts given a 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 a human evaluation performed by medical doctors.