Antibiotic resistance is one of the major concerns in veterinary and human medicine and poses a considerable threat to both human and animal health. It has been shown that over- or misuse of antibiotics is one of the primary drivers of antibiotic resistance. To develop the surveillance of antibiotic use, Switzerland introduced the "Informationssystem Antibiotika in der Veterinärmedizin" (IS ABV) in 2019, mandating electronic registration of antibiotic prescriptions by all veterinarians in Switzerland. However, initial data analysis revealed a considerable amount of implausible data entries, potentially compromising data quality and reliability. These anomalies may be caused by input errors, inaccuracies, incorrect or aberrant master data or data transmission and make analysis impossible. To address this issue efficiently, we propose a two-stage anomaly detection framework utilizing machine learning algorithms. In this study, our primary focus was on cattle treatments with either single or group therapy, as they were the species with the highest prescription volume. However, not all outliers are necessarily incorrect; some may be legitimate but unusual antibiotic treatments. Thus, expert review plays a crucial role in distinguishing outliers, that are correct from actual errors. Initially, relevant prescription variables were extracted and pre-processed with a custom-built scaler. A set of unsupervised algorithms calculated the probability of each data point and identified the most likely outliers. In collaboration with experts, we annotated anomalies and established anomaly thresholds for each production type and active substance. These expert-annotated labels were then used to fine-tune the final supervised classification algorithms. With this methodology, we identified 22,816 anomalies from a total of 1,994,170 prescriptions in cattle (1.1 %). Cattle with no further specified production type had the most (2 %) anomalies with 7758 out of 379,995. The anomalies were consistently identified and comprised prescriptions with too high and too low dosages. Random Forest achieved a ROC-AUC score of 0.994, (95 % CI: 0.992, 0.995) and a F1-Score of 0.962 (95 % CI: 0.958, 0.966) for single treatments. The versatility of this framework allows its adaptation to other species within IS ABV and potentially to other prescription-based surveillance systems. If applied regularly to uploaded prescriptions, it should reduce input errors over time, improving the validity of the data in the long term.

中文翻译：

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兽用抗生素处方监控系统 (IS ABV) 中的异常检测


抗生素耐药性是兽医和人类医学的主要问题之一，对人类和动物健康构成相当大的威胁。研究表明，过度或滥用抗生素是抗生素耐药性的主要驱动因素之一。为了加强对抗生素使用的监测，瑞士于 2019 年推出了“兽医抗生素信息系统”(IS ABV)，要求瑞士所有兽医对抗生素处方进行电子注册。然而，初始数据分析揭示了大量不可信的数据条目，可能会影响数据质量和可靠性。这些异常可能是由输入错误、不准确、不正确或异常的主数据或数据传输引起的，并使分析变得不可能。为了有效地解决这个问题，我们提出了一个利用机器学习算法的两阶段异常检测框架。在这项研究中，我们的主要重点是采用单一或团体治疗的牛治疗，因为它们是处方量最高的物种。然而，并非所有异常值都一定是错误的。有些可能是合法但不寻常的抗生素治疗。因此，专家评审在区分异常值（正确与实际错误）方面发挥着至关重要的作用。最初，提取相关处方变量并使用定制的缩放器进行预处理。一组无监督算法计算每个数据点的概率并识别最可能的异常值。我们与专家合作，对异常情况进行了注释，并为每种生产类型和活性物质建立了异常阈值。然后使用这些专家注释的标签来微调最终的监督分类算法。 通过这种方法，我们从总共 1,994,170 个牛处方中发现了 22,816 个异常情况 (1.1%)。没有进一步指定生产类型的牛异常最多 (2%)，共有 379,995 头牛中的 7758 头出现异常。这些异常现象得到了一致的识别，包括剂量过高和过低的处方。随机森林单次处理的 ROC-AUC 评分为 0.994（95% CI：0.992，0.995），F1 评分为 0.962（95% CI：0.958，0.966）。该框架的多功能性使其能够适应 IS ABV 内的其他物种，并可能适应其他基于处方的监测系统。如果定期应用于上传的处方，随着时间的推移，输入错误应该会减少，从而提高数据的长期有效性。