A not uncommon scenario in rare disease is that of Hao-Fountain Syndrome and Project Artemis. This project launched in 2018 when 34 patients worldwide were known to have this USP7 gene mutation. The goal of the project was to find 66 more patients and get to 100 patients. This last month, three years later, Project Artemis achieved its goal and 100 patients were found.
Because in general these rare disease datasets are small, that makes it difficult to use some of the more sophisticated AI and machine learning techniques, which tend to require larger data population. Synthetic data generation (SDG) techniques can help solve this problem by amplifying these small datasets, and enabling more analytic strategies on these types of data, leading to faster information gain and discoveries.
This webinar provides some background on rare disease datasets and how they are collected, as well as the small data challenges of these datasets. How SDG can help address these small data challenges is explained, and examples of how this works are presented.
