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.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Percentage of Participants With Positive Predictive Value (PPV) at Different Cut-off Values (top 10, 20, 50, 100 and 200)
Timeframe: Up to End of the study (approximately 6 months)