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ThinkRare:一种在电子病历中识别罕见遗传疾病的搜索算法

Grace U Ediae¹, Alexandre White-Brown², Caitlin Chisholm³

¹Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Ontario, Canada; Department of Human Genetics, McGill University, Montreal, Quebec, Canada.

Genetics in medicine : official journal of the American College of Medical Genetics

|August 26, 2025

概括

一种名为ThinkRare的新算法有助于识别未经诊断的罕见遗传疾病 (RGD) 患者进行遗传检测. 这种工具旨在改善复杂疾病的早期诊断和干预.

科学领域:

基因组学
医疗信息学
罕见疾病

背景情况:

没有确诊的罕见遗传疾病 (RGD) 往往没有被识别,从而延迟了关键的遗传检测和诊断.
医疗保健提供者在识别可能受益于基因评估的患者方面面临挑战.

研究的目的:

开发和验证基于规则的搜索算法"ThinkRare",以识别未被诊断的RGD患者.
通过主动识别符合条件的患者来解决延迟诊断的障碍.

主要方法:

使用结构化的电子病历数据和临床标准来识别潜在的RGD病例.
采用代测试和医疗记录审查以优化和验证算法.
通知确诊患者的医生,提供遗传学服务.

主要成果:

追溯对262,296名患者进行了应用,确定了30名符合对外体测序的患者.
估计回忆率 (灵敏度) 为60%和精度 (积极预测值) 为15%.
在转诊和评估的患者中,诊断率达到了50% (4/8).

结论:

通过ThinkRare算法有效地追溯识别了RGD患者.
未来的部署可以帮助临床医生识别和转诊罕见病患者,促使早期诊断.

关键词:

人工智能电子医疗记录罕见的疾病

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