Computer-assisted patient identification tool in inborn errors of metabolism - potential for rare disease patient registry and big data analysis.

ABSTRACT

BACKGROUND: Patient registries are crucial for rare disease management. However, manual registry construction is labor-intensive and often not user-friendly. Our goal is to establish Hong Kong's first computer-assisted patient identification tool for rare diseases, starting with inborn errors of metabolism (IEM). METHODS: Patient data from 2010 to 2019 was retrieved from electronic databases. Through big data analytics, patient data were filtered based on specific IEM-related biochemical and genetic tests. Clinical notes were analyzed using a rule-based natural language processing technique called regular expression. The algorithm classified each extracted paragraph as "IEM-related" or "not IEM-related." Pathologists reviewed the paragraphs for curation, and the algorithm's performance was evaluated. RESULTS: Out of 46,419 patients with IEM-related tests, the algorithm identified 100 as "IEM-related." After pathologists' validation, 96 cases were confirmed as true IEM, with 1 uncertain case and 3 false positives. A secondary ascertainment yielded a sensitivity of 92.3% compared to our previously published IEM cohort. CONCLUSIONS: Our 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 potentially be accessed by multiple stakeholders for collaborative research and to enhance healthcare management for rare diseases.