Transformer models trained on MIMIC-III to generate synthetic patient notes

Natural Language Processing can help to unlock knowledge in the vast troves of unstructured clinical data that are collected during patient care. Patient confidentiality presents a barrier to the sharing and analysis of such data, however, meaning that only small, fragmented and sequestered datasets are available for research. To help side-step this roadblock, we explore the use of Transformer models for the generation of synthetic notes. We demonstrate how models trained on notes from the MIMIC-III clinical database can be used to generate synthetic data with potential to support downstream research studies. We release these trained models to the research community to stimulate further research in this area.