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.