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很少有人对罕见遗传疾病患者的表型驱动诊断进行学习

Emily Alsentzer^1,2, Michelle M Li^1,3, Shilpa N Kobren¹

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

NPJ digital medicine

|June 20, 2025

概括

创新的深度学习方法SHEPHERD通过分析知识图表来帮助诊断罕见疾病. 这种方法克服了数据的局限性,加速了未被诊断的患者的诊断过程.

科学领域:

医疗信息学医疗信息学
基因组学就是基因组学.
人工智能的人工智能

背景情况:

有超过7000种罕见疾病存在,由于临床经验有限和症状多样化,许多疾病未被诊断出来.
目前的深度学习诊断工具需要大量的患者数据,不适合罕见疾病.

研究的目的:

推出SHEPHERD,一个用于多方面的罕见疾病诊断的几次学习框架.
在罕见疾病诊断的知识图表上利用深度学习.

主要方法:

开发了SHEPHERD,一种使用知识图表和罕见疾病数据丰富的几次学习方法.
在模拟的罕见病患者数据上训练模型.
在因果基因发现,患者队列检索和新型疾病呈现特性中得到验证.

主要成果:

证明了SHEPHERD在未诊断疾病网络,MyGene2和解密发育障碍研究中的现实世界队列中的有效性.
展示了进行因果基因发现和识别类似患者的能力.
成功描述了新型疾病的表现.

结论:

在加速罕见疾病诊断方面,SHEPHERD显示出前景.
基于知识的深度学习为数据稀缺的罕见疾病诊断提供了可行的解决方案.

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