机器学习与阿尔法毒素现型,以预测患者的临床结果与金黄色葡萄球菌血流感染
Brent Beadell1, Surya Nehra2, Elizabeth Gusenov1
1Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA.
Toxins
|July 28, 2023
概括
机器学习可以预测黄金菌血流感染的结果. 分析细菌图像和血小板计数有助于预测血小板缺血和死亡率,指导治疗决策.
科学领域:
- 微生物学 微生物学
- 传染性疾病 传染性疾病
- 机器学习在医学中的应用
背景情况:
- 黄金葡萄球菌 (Staphylococcus aureus) 血流感染 (SAB) 是败血症死亡的主要原因.
- 目前的治疗方法没有考虑像α-toxin (Hla) 这样的毒性因素,这会导致血小板缺血.
- 在SAB中预测患者的结果具有挑战性.
研究的目的:
- 将机器学习 (ML) 应用于细菌生长图像和临床数据,以预测SAB中的患者结果.
- 评估SAB中Hla表型的预后意义.
- 开发一种ML模型,用于预测SAB患者的血小板缺血和死亡率.
主要方法:
- 用智能手机拍摄的羊血甲菌的白血解菌生长图像被用来描述Hla表型.
- 一个卷积神经网络处理了图像数据和第一天的血小板计数.
- 经过ML模型的训练,可以预测第四天的血小板缺血和死亡率.
主要成果:
- 这项研究包括229名SAB.患者.
- 在ML模型中,在第4天预测血小板狭窄的AUC为0.92,可预测血小板狭窄.
- 模型在预测死亡率方面表现不佳 (AUC为0.711).
结论:
- 对细菌毒性因子的ML分析可以预测SAB.患者的结果.
- 这种方法为数字微生物学应用提供了概念验证.
- 预测血小板缺血和死亡率可以帮助指导SAB的治疗选择.
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