机器学习用于估计和比较使用抗生素治疗腹疾病的临床规则
Allison Codi1, Sara Kim2, Elizabeth Rogawski McQuade2
1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Georgia, USA.
medRxiv : the preprint server for health sciences
|January 20, 2025
概括
制定针对儿童腹的个性化治疗规则可以帮助限制过度使用抗生素并制抗菌素耐药性. 这项研究提供了一个新的框架,用于创建和评估这些个性化治疗指南,以获得更好的患者结果.
科学领域:
- 儿科 儿科 儿科
- 传染性疾病 传染性疾病
- 生物统计学 生物统计学
背景情况:
- 急性腹疾病是五岁以下儿童死亡的主要原因,特别是在资源较低的地区.
- 导致腹的细菌感染可能可以用抗生素治疗,但广泛使用可能会导致抗菌素耐药性.
- 需要个性化治疗指南,以平衡治疗效益与耐药性风险.
研究的目的:
- 开发和评估一个框架,为儿童水性腹制定个性化治疗规则.
- 将诊断和临床信息纳入个性化抗生素建议.
- 为了明确限制过度处理和减轻抗菌素耐药性的出现.
主要方法:
- 使用了一个框架来创建和评估个性化治疗规则.
- 通过嵌套交叉验证采用集体机器学习和双强度估计.
- 拟议的方法来比较基于不同协变量集的规则,量化生物标志物影响.
主要成果:
- 在一个现实的模拟研究中证明了适当的推断.
- 将该方法应用于严重腹 (ABCD) 儿童抗生素试验的现实数据.
- 展示了能够量化额外诊断生物标志物的影响的能力.
结论:
- 拟议的框架有效地推导和评估儿童腹的个性化治疗规则.
- 这种方法有助于优化抗生素的使用,减少耐药性风险.
- 这些方法允许评估诊断生物标志物在临床决策中的价值.
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