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表型驱动的分子遗传测试建议用于诊断儿科罕见疾病

Fangyi Chen¹, Priyanka Ahimaz^2,3, Quan M Nguyen^4,5

¹Department of Biomedical Informatics, Columbia University, New York, NY, USA.

NPJ digital medicine

|November 21, 2024

概括

一个新的机器学习模型帮助儿科医生选择适合罕见疾病的基因测试. 这种工具有助于更快的诊断,推基于患者表型的外体或基因组测序.

科学领域:

遗传学和基因组学遗传学和基因组学
机器学习在医学中的应用
罕见疾病的诊断罕见疾病的诊断

背景情况:

罕见病患者面临显著的诊断延迟.
基因检测至关重要,但对于非专家来说是复杂的.
目前的指导方针建议基于临床表现的外体/基因组测序或基因面板.

研究的目的:

开发一种机器学习模型,用于推适当的遗传测试.
帮助一般儿科医生在复杂的基因测试决策中进行导航.
为了加快罕见疾病的诊断.

主要方法:

在来自哥伦比亚大学欧文医疗中心的1005个患者记录上训练了一种机器学习模型.
该模型利用患者的表型信息来推遗传测试.
使用接收器操作特征曲线 (AUROC) 下面的面积和精度召回曲线 (AUPRC) 下面的面积来评估性能.

主要成果:

该模型在培训队列中实现了AUROC 0.823和AUPRC 0.918.
该模型在外部队列中显示出强大的通用性,AUROC:0.77和AUPRC:0.816.
模型的性能与遗传学专家的决定密切相关.

结论:

开发的机器学习模型有效地建议用于罕见疾病诊断的基因测试.
该工具显示有潜力帮助一般儿科医生改善诊断时间表.
这种方法可以增强遗传测试订单,加快罕见疾病的识别.

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