Early Detection of Fabry Disease

Early Detection of Fabry Disease (FD): Using Real-World Data for the Development of Advanced Natural Language Processing Methods: Retrospective Database Analysis

The main aim of this study is early detection of FD using real-world data for the development of advanced natural language processing methods and to develop a predictive algorithm and to measure the performance of the algorithm in identifying participants with FD.

This study is about using data from hospital Electronic Health Record database from the last 10 years to describe the ranking of participants with FD using multilevel likelihood ratios and to validate the algorithm using positive controls. No investigational medicinal product or device will be tested in this study. Hospital electronic health record data will be analyzed for a period of up to 6 months.

Inclusion criteria:

• In-patient or out-patient datasets of the participating hospital in the last 10 years
• Participants at any age Positive controls: a subset of all participant hospital records that includes the participants with confirmed FD.