实时机器学习预测下一代测序测试结果在实时临床环境中
Grace Y E Kim1, Matthew Schwede2,3, Conor K Corbin2,3
1Stanford Center for Biomedical Informatics Research, Stanford, CA, USA. grkim0987@alumni.stanford.edu.
NPJ digital medicine
|August 19, 2025
概括
机器学习可以指导新型遗传测试的使用,如血液癌症的Heme-STAMP. 这种方法有助于临床决策,提供与专家血液学家可比的准确性.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 基因组学就是基因组学.
- 医疗保健中的机器学习
背景情况:
- 下一代测序 (NGS) 试验提供先进的诊断,但由于新性和成本,临床采用面临挑战.
- 血红-STAMP试验识别了与血液淋巴瘤瘤相关的基因突变.
研究的目的:
- 开发和整合一个机器学习 (ML) 模型,以协助临床决策,以订购Heme-STAMP测试.
- 评估ML模型在预测NGS测试结果方面与专家血液学家的性能.
主要方法:
- 从一个学术医疗中心使用3472个Heme-STAMP测试订单训练了一个ML模型 (2018年5月 - 2021年9月).
- 将定制的ML模型集成到实时临床环境中,以实时预测和与医生估计进行比较.
主要成果:
- 在预测Heme-STAMP测试结果 (AUC:0.77-0.78) 中,ML模型实现了与专家血液学家相比较的区分能力.
- 该模型表明,它有可能提高复杂的NGS测试中人类专家估计的校准.
结论:
- 机器学习提供了一个可行的策略来指导先进的基因组测试的临床实用性,如Heme-STAMP.
- 整合ML工具可以支持基于证据的决策,优化血液学中昂贵的诊断技术的使用.
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