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.

Data from all in-patient or out- patient datasets of the participating hospital in the last 10 years will be used to test the forecasting power of the algorithm.

Takeda provides access to the de-identified individual participant data (IPD) for eligible studies to aid qualified researchers in addressing legitimate scientific objectives (Takeda's data sharing commitment is available on https://clinicaltrials.takeda.com/takedas-commitment?commitment=5). These IPDs will be provided in a secure research environment following approval of a data sharing request, and under the terms of a data sharing agreement.

IPD from eligible studies will be shared with qualified researchers according to the criteria and process described on https://vivli.org/ourmember/takeda/ For approved requests, the researchers will be provided access to anonymized data (to respect patient privacy in line with applicable laws and regulations) and with information necessary to address the research objectives under the terms of a data sharing agreement.