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半监督校准噪音事件风险 (SCANER) 使用电子健康记录

Chuan Hong¹, Liang Liang², Qianyu Yuan²

¹Duke University, Durham, NC, USA; Harvard Medical School, Boston, MA, USA.

Journal of biomedical informatics

|June 18, 2023

概括

这项研究引入了SCANER,这是一种新的方法,可以从噪音严重的电子健康记录 (EHR) 数据中准确估计存活率. 扫描仪提高了生存分析的效率和稳定性,克服了传统方法的局限性.

科学领域:

生物统计学生物统计学
医疗信息学医疗信息学
临床研究临床研究

背景情况:

电子健康记录 (EHR) 为预测建模提供了丰富的数据,但往往含有不可靠的研究信息,特别是在无进展生存 (PFS) 等生存结果方面.
由于依赖于自由文本笔记和行政数据,从电子健康记录中精确地提取事件状态和时间是具有挑战性的,这导致了生存率估计的潜在偏差.
手动注释准确的生存数据是资源密集型,需要自动化和可靠的方法.

研究的目的:

开发一个校准的生存率估计器,有效地利用来自EHR的噪音结果数据.
为了应对从复杂的EHR数据中提取准确的事件时间和状态以进行生存分析的挑战.
提高EHR相关研究中生存率估计的准确性和效率.

主要方法:

提出了噪音事件率 (SCANER) 估计器的两阶段半监督校准.
利用一小组手动注释的黄金标准生存结果和一大组自动从EHR中提取的代理特征.
验证了肺癌患者无进展生存率 (PFS) 和COVID-19患者无ICU生存率的SCANER估计器.

主要成果:

扫描仪表现出与完整案例卡普兰-梅尔 (KM) 估计器相似的点估计,但效率明显提高 (标准误差减少高达50%).
没有考虑诱导依赖的基准方法在所有案例研究中产生了偏差结果.
与其他方法相比,SCANER被证明更强大,并且对归算模型的错误规范不那么敏感.

结论:

扫描仪估计器提供比现有方法更高效,更强大,更准确的生存率估计.
这种方法通过利用多个替代标签来提高事件时间估计的分辨率.
扫描仪有望在电子健康记录研究中改善生存分析,特别是在复杂或编码不良的疾病中.

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