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机器学习模型的通用性,以改善在多个临床中心对甲状腺激素相关测试的利用

He S Yang¹, Weishen Pan², Yingheng Wang³

¹Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States.

Clinical chemistry

|September 22, 2023

概括

机器学习 (ML) 可以改善副甲状腺激素相关 (PTHrP) 测试的使用. 直接将ML模型应用于新数据可能会失败,但重新训练或重建模型可以提高准确性,以便做出更好的临床决策.

科学领域:

在瘤学瘤学.
生物化学生化学
医疗信息学医疗信息学

背景情况:

甲状腺激素相关 (PTHrP) 测试有助于诊断恶性瘤的幽默性高热血症.
当前的PTHrP订单实践往往缺乏预测概率,导致测试利用效率低下.
手动审查PTHrP结果以识别不合适的订单是耗时的.

研究的目的:

开发和评估用于预测异常PTHrP结果的机器学习 (ML) 模型.
评估改善ML模型在不同数据集中的通用性的策略.
通过自动化结果审查,提高PTHrP测试的临床效用.

主要方法:

开发了一个ML模型,使用1330名患者的数据集来预测异常PTHrP结果.
在两个外部数据集上评估模型性能,采用模型传输,再培训,重建和微调等策略.
利用最大平均差异 (MMD) 来量化数据集之间的数据分布转移.

主要成果:

最初的ML模型在开发队列中实现了0.936的AUROC.
将模型直接传输到外部数据集下降了AUROC到0.838和0.737.
用特定站点数据重建模型将AUROC提高到0.891和0.837;微调也提高了有限的外部数据的实用性.

结论:

机器学习有可能优化PTHrP测试利用率并减少手动审查负担.
直接在不同机构中应用ML模型可能会导致由于数据转移而导致性能下降.
如果有足够的数据,模型重新训练或重建是有效的,而微调对于有限的特定站点数据是有益的.

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