经季节调整的实验室参考间隔,以提高机器学习模型对心血管疾病分类的性能
Victorine P Muse1, Davide Placido1, Amalie D Haue1,2
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark.
BMC medical informatics and decision making
|March 4, 2024
概括
实验室参考间隔的季节性调整改善了用于心血管疾病分类的机器学习模型. 这一数据预处理步骤提高了诊断准确度,特别是对于血管疾病.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 实验室医疗保健数据的季节性变化很常见,但往往没有得到解决.
- 在医疗保健数据中缺乏对季节性的系统计算.
- 这项研究引入了一种新的调整方法,用于实验室数据的季节性变化.
研究的目的:
- 分析季节性对机器学习模型疾病分类的影响.
- 评估一种用于调整实验室参考间隔 (RIs) 季节性变化的新方法.
- 评估季节性调整后诊断分类准确度的提高.
主要方法:
- 在约2200万个患者记录上训练并测试了四种机器学习模型 (adaBoost,决策树,神经网络,随机森林).
- 使用ICD-10代码对35种循环系统疾病进行分类.
- 应用季节性调整到23个实验室参考间隔 (RIs) 在模型训练之前和之后.
- 使用超参数优化和性能指标 (AUROC,AUPRC) 的基准调整效应.
主要成果:
- 根据季节性调整的RI显著改善了使用神经网络模型测试的35种疾病中的24种心血管疾病的分类.
- 诸如C反应蛋白和估计的膜过等特征具有很高的重要性.
- 血管疾病的分类准确性,包括血栓和动脉样硬化疾病,在调整后持续改善.
结论:
- 通过季节性调整等预处理来提高数据质量,对于先进的数据驱动医疗保健至关重要.
- 提出的方法可以将季节性调整的RI整合到各种疾病领域的临床研究中.
- 季节性调整的RI提高了诊断分类,应考虑用于临床决策支持系统.
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