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在败血症患者中使用血液常规指标与人工智能相结合的价值

Jianhui Chen¹, Minghuan Huang², Rongbin Xu³

¹Department of Critical Care Medicine, Affiliated Hospital of Putian University Putian 351100, Fujian, China.

American journal of translational research

|May 19, 2025

概括

机器学习算法 (MLA) 使用血液常规指标 (BRI) 有效预测败血症. 随机森林分类模型 (RFCM) 显示了最高的准确性,红细胞分布宽度 (RDW) 是一个关键预测因素.

科学领域:

生物医学信息学生物医学信息学
临床诊断临床诊断临床诊断
计算生物学计算生物学

背景情况:

败血症的诊断仍然具有挑战性,需要改进早期检测方法.
血液常规数据 (BRD) 为诊断标记提供了一个易于获得的来源.
机器学习算法 (MLA) 在分析复杂的医疗数据以预测疾病方面表现有前途.

研究的目的:

调查血液常规指标 (BRI) 和败血症之间的相关性.
评估各种MLA在早期败血症预后中的有效性.
确定关键的BRI特征,这些特征对败血症预测具有重要意义.

主要方法:

分析了来自败血症和常见感染患者的4,558个BRD样本.
七个预测模型的开发和比较:二元逻辑回归 (BLRM) 和六个MLA (SVM,NN,贝叶斯,k-NN,决策树,RFCM).
使用准确度,精度,回忆和F1分数进行性能评估.

主要成果:

随机森林分类模型 (RFCM) 取得了最高的性能 (准确率:86.97%,F1:0.87),明显超过了BLRM (准确率:68.77%,F1:0.70).
红细胞分布宽度 (RDW) 成为RFCM中最重要的预测指标.
主要因素是RDW变化系数 (6.98%) 和RDW标准偏差 (5.32%).

结论:

将BRI与MLA集成为败血症预测提供了巨大的潜力.
该RFCM证明了早期败血症检测的卓越预测价值.
RDW是预测败血症的关键指标,需要进一步调查.

关键词:

塞普西 (Sepsi) 是一种疾病.血液常规的血液常规机器学习算法的算法预测预测预测预测

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