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分析偏差对用于使用实验室数据预测败血症的机器学习模型的影响
Meryem Rumeysa Yesil1, Ilaria Talli2,3, Michela Pelloso3
1Department of Medical Biochemistry, University of Health Sciences, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Türkiye.
Clinical chemistry and laboratory medicine
|May 29, 2025
概括
实验室数据中的分析偏差可能会影响机器学习的败血症预测模型. 虽然白细胞,血小板和ESR偏差对模型性能的影响很小,但为了可靠性,需要新的验证策略.
科学领域:
- 临床实验室科学 临床实验室科学
- 生物医学信息学是生物医学信息学.
- 机器学习在医疗保健中的应用.
背景情况:
- 机器学习 (ML) 模型越来越多地用于利用实验室数据预测早期的败血症.
- 实验室测量中的分析偏差可能会对这些ML模型的性能和现实世界的有效性产生负面影响.
- 评估可接受的分析偏差的影响对于强大的ML模型开发至关重要.
研究的目的:
- 评估实验室测量中的分析上可接受的偏差如何影响用于败血症预测的ML模型的有效性和概括性.
- 量化白细胞 (WBC),血小板 (PLT) 和红细胞沉率 (ESR) 的偏差对模型性能的影响.
主要方法:
- 用完整血清和ESR数据开发了一个用于败血症预测的支持矢量机 (SVM) 模型.
- 基于分析性能规范,为WBC,PLT和ESR生成了26个偏差组合.
- 在偏差条件下的SVM模型的诊断性能 (AUC) 与原始数据集进行了比较.
主要成果:
- 原来的SVM模型实现了90.6%的AUC.
- 对WBC (2.67.7%),PLT (2.26.7%) 和ESR (10.531.6%) 定义了可接受的偏差水平.
- 在测试的偏差条件中,AUC范围从87.8%到90.4%,没有观察到统计学上显著的差异 (p>0.05).
结论:
- 虽然单个或组合实验室参数的分析上可接受的偏差对本研究中的SVM模型性能产生了有限的影响,但偏差可以影响模型结果.
- 开发先进的验证策略对于严格评估分析偏差对ML模型中使用的实验室数据的影响至关重要.
- 改进的验证方法将提高基于ML的败血症预测工具的可靠性和临床实用性.
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