使用生物标志物和感染部位,在老年人败血症中随机森林区分大肠杆菌
Bu-Ren Li1, Ying Zhuo1, Ying-Ying Jiang1
1Department of Clinical Laboratory, Fuding Hospital, Fujian University of Traditional Chinese Medicine, 120 South Road of Old City, Fuding, 355200, Fujian, China.
Scientific reports
|June 5, 2024
概括
这项研究使用机器学习准确诊断老年患者的败血症亚型,区分大肠杆菌和非大肠杆菌感染. 开发的模型显示了高准确性,改善了败血症诊断和精确医学.
科学领域:
- 传染性疾病 传染性疾病
- 老年病的医生 老年病的医生
- 计算生物学 计算生物学
背景情况:
- 在老年患者中,准确的败血症亚型诊断是具有挑战性的.
- 区分大肠杆菌 (大肠杆菌) 和非大肠杆菌感染对于向治疗至关重要.
- 老年败血症患者具有独特的诊断复杂性.
研究的目的:
- 开发和验证一种机器学习模型,用于准确诊断老年人血症亚型.
- 确定主要的临床生物标志物和感染部位,可预测败血症亚型.
- 为了提高老年败血症患者的诊断精度.
主要方法:
- 对119名老年败血症患者的回顾性分析.
- 利用随机森林模型来评估临床生物标志物和感染地点.
- 使用后勤回归和最小绝对收缩和选择操作员 (LASSO) 分析.
主要成果:
- 随机森林模型的整体准确率为87.5%,精度为93.3%,回忆率为87.5%.
- 发现的关键预测因素包括感染部位,血小板分布宽度,血小板数量低,以及prcalcitonin.
- 该模型显示F1评分为90.3%,AUC为88.0%,用于区分败血症亚型.
结论:
- 机器学习模型对提高老年人败血症诊断的准确性显示出显著的希望.
- 识别特定的感染部位和生物标志物有助于区分败血症亚型.
- 这种方法在老年人传染病管理中推进了精准医学.
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