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随机森林模型在预测抗药多种药物抗性格拉姆阴性细菌前体感染方面的有效性与传统的物流回归模型相比

Jinglan Deng¹, Yongchun Ge², Lingli Yu³

¹School of Nursing, Xinjiang Medical University, Urumqi, China.

Microbial drug resistance (Larchmont, N.Y.)

|April 15, 2024

概括

随机森林 (RF) 模型在预测多药耐药性格拉姆阴性细菌感染方面明显优于传统的物流回归 (LR). 射频模型显示出更高的精度和灵敏度,表明其更大的临床潜力.

科学领域:

医疗信息学医疗信息学
传染性疾病传染性疾病
医疗保健中的机器学习

背景情况:

医院感染 (HAI) 构成重大威胁,特别是那些由多药耐药 (MDR) 格拉姆阴性细菌引起的.
准确预测MDR格拉姆阴性细菌HAI对于及时干预和改善患者结果至关重要.
传统的统计模型在捕捉患者数据中的复杂模式方面存在局限性.

研究的目的:

为了将随机森林 (RF) 模型的预测性能与医院获得的MDR 格兰阴性细菌感染的物流回归 (LR) 模型进行比较.
确定最有效的机器学习方法来预测这些具有挑战性的感染.

主要方法:

一项回顾性研究,涉及541名在两个三级医院的格拉姆阴性细菌HAI患者.
数据包括感染前的临床信息和药物历史.
开发和验证了RF和LR模型,使用7:3数据分割.

主要成果:

与LR (84.57%) 相比,RF模型获得了更高的精度 (89.51%).
射频显示出优越的灵敏度 (90.79%与82.89%) 和特异性 (88.37%与80.10%).
在ROC曲线下的面积对于RF (0.94) 比LR (0.91) 高.

结论:

随机森林模型为医院获得的MDR格兰氏阴性细菌感染提供了优越的预测性能,而不是物流回归.
射频模型的提高准确性和灵敏性表明它们在感染预测中具有更大的临床应用潜力.
像RF这样的机器学习方法在打击医疗机构中抗生素耐药性日益增长的威胁方面表现有前途.

关键词:

逻辑回归模型的逻辑回归模型.多药耐药的格拉姆阴性细菌医院内感染的疾病.随机森林模型随机森林模型

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