A Statistical Solution to Synthetic Data Generation for Patient Files
The project I created for the FHIR hackathon is a mathematically driven library which generates a DataFrame of patient files given input data, which preserves statistical properties of the original data. The type of statistics employed is an important demonstrator of a method of generating synthetic data given a small initial dataset to base it on, and is a technique that will generally surpass widely used (but data-intensive) deep learning techniques on these types of smaller datasets.
