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在兽医抗生素处方监测系统 (IS ABV) 中检测异常

Guy-Alain Schnidrig¹, Anaïs Léger², Heinzpeter Schwermer²

¹Veterinary Public Health Institute, Vetsuisse, University of Bern, Schwarzenburgstrasse 161, Liebefeld 3097, Switzerland; Graduate School of Cellular and Biomedical Sciences, University of Bern, Hochschulstrasse 4, Bern, Switzerland.

Preventive veterinary medicine

|July 28, 2024

概括

这项研究开发了一种机器学习框架,用于检测兽医抗生素处方中的异常,确定1.1%的牛治疗异常. 这提高了对抗生素耐药性监测的数据质量.

科学领域:

兽医医学兽医医学兽医医学
数据科学是数据科学.
流行病学流行病学

背景情况:

抗生素耐药性是对人类和动物健康的重大威胁,其主要原因是过度使用抗生素.
瑞士的IS ABV系统要求对兽医抗生素处方进行电子注册.
最初的数据分析显示出不可思议的条目,影响了数据质量.

研究的目的:

开发和验证用于检测兽医抗生素处方异常的机器学习框架.
提高IS ABV监控系统中数据的质量和可靠性.
为了识别和量化牛群中不可思议的抗生素处方.

主要方法:

一个两阶段的异常检测框架,使用无监督和监督的机器学习算法.
使用自定义缩放器和专家对异常的注释进行数据预处理.
使用随机森林进行验证,实现高ROC-AUC和F1分数.

主要成果:

在1,994,170个牛处方中确定了22,816个异常 (1.1%).
没有指定生产类型的牛显示出最高的异常率 (2%).
异常包括不正确的高和低剂量;随机森林实现了0.994的ROC-AUC和0.962的F1-Score.

结论:

拟议的框架有效地检测兽医抗生素处方中的异常.
专家审查对于区分真正的错误和合法但不寻常的治疗方法至关重要.
定期应用该框架可以提高监控系统中的数据有效性.

关键词:

使用抗生素使用抗生素牛群是牛群,牛群是牛群,牛群是牛群.这是ABV ABV.机器学习机器学习监督监督监督监督监督监督监督监督监督监督监督监督

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