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.
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.
摘要：
背景：患者登记对于罕见疾病管理至关重要。然而,手动注册表建设是劳动密集型的，不方便用户。我们的目标是建立香港首个计算机辅助的罕见疾病患者识别工具，从先天性代谢错误(IEM)开始。
方法：从电子数据库检索2010年至2019年的患者数据。通过大数据分析，根据特定的IEM相关生化和/或基因检测对患者进行筛选.使用称为正则表达式的基于规则的自然语言处理技术分析临床笔记。算法将每个提取的段落分类为\"IEM相关\"或\"不与IEM相关。“病理学家审查了这些段落，并评估了算法的性能。
结果：在46,419名IEM相关测试患者中，该算法确定100为“与IEM相关”。“经过病理学家的验证，96例确诊为真IEM，1例不确定病例和3例假阳性。与我们先前发表的IEM队列相比，二次确定的灵敏度为92.3％。
结论：我们的人工智能方法提供了一种新的方法来识别IEM患者，促进创建一个集中的，地方和国家层面的计算机辅助罕见病患者登记。多个利益相关者可以访问这些数据，以进行合作研究，并加强罕见疾病的医疗保健管理。
方法：从电子数据库检索2010年至2019年的患者数据。通过大数据分析，根据特定的IEM相关生化和/或基因检测对患者进行筛选.使用称为正则表达式的基于规则的自然语言处理技术分析临床笔记。算法将每个提取的段落分类为\"IEM相关\"或\"不与IEM相关。“病理学家审查了这些段落，并评估了算法的性能。
结果：在46,419名IEM相关测试患者中，该算法确定100为“与IEM相关”。“经过病理学家的验证，96例确诊为真IEM，1例不确定病例和3例假阳性。与我们先前发表的IEM队列相比，二次确定的灵敏度为92.3％。
结论：我们的人工智能方法提供了一种新的方法来识别IEM患者，促进创建一个集中的，地方和国家层面的计算机辅助罕见病患者登记。多个利益相关者可以访问这些数据，以进行合作研究，并加强罕见疾病的医疗保健管理。
