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数据驱动的早期预警方法用于基于高水平指标的抗菌素耐药性预测-异常检测

Szilveszter Csorba^1,2, Krisztián Vribék^1,2, Máté Farkas^1,2

¹Department of Digital Food Science, Institute of Food Chain Science, University of Veterinary Medicine, H-1078 Budapest, Hungary.

Veterinary sciences

|October 28, 2025

概括

农药使用和土地变化等环境因素可以信号抗菌素耐药性 (AMR) 风险. 这项研究开发了一个框架来检测不寻常的环境模式,有助于早期抗药性耐药性监测和风险识别.

科学领域:

环境科学环境科学
公共卫生公共卫生
数据科学数据科学数据科学

背景情况:

抗菌素耐药性 (AMR) 的出现与环境条件有关.
早期检测高风险的AMR情况是具有挑战性的.
环境因素需要综合分析来评估AMR风险.

研究的目的:

开发一个数据驱动的框架,用于识别与抗菌素耐药性风险相关的异常环境概况.
检测不寻常的环境模式,表明潜在的AMR热点.
为AMR监测提供早期预警策略.

主要方法:

使用无监督异常检测方法 (隔离森林).
将该方法应用于多变量环境指标:农药使用,土地利用变化,降雨量和作物类型.
分析环境数据以确定没有先前AMR数据的异常配置文件.

主要成果:

农药使用,人口密度,土地利用变化和肥料应用被确定为主要的环境因素.
这些因素解释了异常得分变化的很大一部分.
肥料和农药强度强烈地影响了异常的环境特征,突出了它们在抗菌药物耐药性风险中的作用.

结论:

开发的框架可以确定AMR的全球驱动因素和取决于环境的风险.
可解读的异常检测有助于理解环境对抗药性反应的贡献.
该框架支持开发主动的,数据驱动的AMR监测策略.

关键词:

抗菌药物反症 (AMR) 是一种这就是 SHAP SHAP 的意思.环境驱动因素环境驱动因素在iForest的森林.一个健康的健康.

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