基于机器学习的预测算法的翻译为个性化的实证抗生素选择:基于人口的队列研究
Chungsoo Kim1, Young Hwa Choi2, Jung Yoon Choi3
1Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.
International journal of antimicrobial agents
|September 16, 2023
概括
预测模型可以帮助选择抗生素用于医院获得的尿路感染 (HA-UTIs). 这些模型预测抗生素不敏感性,帮助临床医生选择有效的实证治疗HA-UTIs.
科学领域:
- 传染性疾病 传染性疾病
- 临床微生物学 临床微生物学
- 医疗信息学 医疗信息学
背景情况:
- 在医院获得的尿路感染 (HA-UTIs) 在医疗保健环境中构成了重大挑战.
- 准确和及时选择实证抗生素对于有效的HA-UTI管理至关重要.
- 预测抗生素不敏感性可以指导初始治疗选择.
研究的目的:
- 开发和验证可疑HA-UTI患者抗生素不敏感性的预测模型.
- 支持临床医生选择适当的经验性抗生素治疗.
- 通过优化抗生素选择来改善患者的治疗结果.
主要方法:
- 使用拉索逻辑回归 (LLR),极端梯度增强 (XGB),随机森林和堆叠组合方法开发预测模型.
- 利用了患者人口统计数据,临床数据和2010-2021年的抗生素图谱信息.
- 使用接收器操作特征曲线 (AUROC) 下面的面积来评估模型性能.
主要成果:
- 堆叠组合模型在预测对几种抗生素的不敏感性方面表现出强的表现,包括伊米 (AUROC 80.6).
- 节的模型,特别是使用LLR的模型,表现出良好的临床实用性预测能力.
- 一个基于节模式的个性化应用程序被开发和发布.
结论:
- 对抗生素不敏感性的预测模型是指导HA-UTI病例经验性抗生素选择的有效工具.
- 这些模型可以帮助优化抗生素治疗,并可能减少耐药性的发展.
- 开发的模型和应用程序为临床决策支持提供了宝贵的资源.
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