Generating high-fidelity synthetic patient data for assessing machine learning healthcare software.

ABSTRACT: There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating syn

SUBMITTER: Tucker A 

PROVIDER: S-EPMC7653933 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature