Patient registries are crucial for rare disease management. However, manual registry construction is labor-intensive and not user-friendly. The aim of this study is to establish Hong Kong’s first computer-assisted patient identification tool for rare diseases, starting with Inborn errors of metabolism (IEM).
Patient data from 2010 to 2019 is retrieved from electronic databases. Through big data analytics, patients are filtered based on specific IEM-related biochemical and/or genetic tests. The algorithm classifies each extracted paragraph as “IEM-related” or “not IEM-related.”
Out of 46,419 patients with IEM-related tests, the algorithm identifies 100 as “IEM-related.” After pathologists’ validation, 96 cases are confirmed as true IEM, with 1 uncertain case and 3 false positives. A secondary ascertainment yields a sensitivity of 92.3% compared to the previously published IEM cohort.
This artificial intelligence approach provides a novel method to identify IEM patients, facilitating the creation of a centralized, computer-assisted rare disease patient registry at the local and national levels. This data can be accessed by multiple stakeholders for collaborative research and to enhance healthcare management for rare diseases. Read the full article here.