基于机器学习的delta检查方法用于检测瘤标志物测试中的错误识别错误
Hyeon Seok Seok1, Yuna Choi2, Shinae Yu3
1Interdisciplinary Program of Biomedical Engineering, Chonnam National University, Yeosu, Republic of Korea.
Clinical chemistry and laboratory medicine
|December 14, 2023
概括
机器学习的三角值检查,特别是深度神经网络 (DNN) 模型,显著改善了检测瘤标志物样本误识错误的情况. 这种先进的方法在关键诊断测试的准确性和可靠性方面超过了传统方法.
科学领域:
- 临床化学 临床化学
- 生物信息学是一种生物信息学.
- 医学诊断 医学诊断 医学诊断
背景情况:
- 瘤标志物测试错误识别对患者的诊断和治疗构成重大风险.
- 传统的三角洲检查方法在准确检测这些错误方面存在局限性.
研究的目的:
- 开发和评估基于机器学习 (ML) 的 delta 检查方法,用于检测瘤标志物测试中的样本错误识别.
- 将ML模型的有效性与传统的三角洲检查方法进行比较.
主要方法:
- 对五种瘤标志物的246,261条记录进行分析:AFP,CA19-9,CA125,CEA和PSA.
- 使用随机森林 (RF) 和深度神经网络 (DNN) 算法开发机器学习模型.
- 使用in silico模拟对ML模型进行比较,其中包括delta百分比变化 (DPC),绝对DPC (absDPC) 和参考变化值 (RCV).
主要成果:
- 在所有测试的瘤标志物 (0.792-0.842) 中,DNN模型显示出卓越的平衡精度.
- 在检测错误识别错误方面,DNN模型的表现优于RF,DPC,absDPC和RCV.
- 射频模型显示混合性能,通常比DPC和absDPC好,但比RCV可比或低.
结论:
- 基于ML的三角检查方法比常规方法更有效地检测瘤标志物样本的错误识别.
- DNN模型提供了一种强大而稳定的解决方案,用于提高瘤标志物测试的准确性.
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