Semi-Synthetic Trauma Resuscitation Process Data Generator

Process mining techniques have been applied to the visualization, interpretation, and analysis of medical processes. However, only a very limited amount of process data necessary for these analyses is publicly available, especially in the medical field because of patients' privacy. This limits novel medical process research to using insufficiently large or randomly-generated synthetic datasets. Our goal in this study is to train a model (using a limited amount of observed process data) that can generate large amounts of semi-synthetic process data. This generated data has characteristics similar to those of real-world process data, and could potentially be observed in reality.