机器学习用于检测亚临床乳腺炎:贝叶斯的方法包含诊断测试特性
Charlott Olofsson1, Aliaksandr Hubin2, Hilde Vinje2
1Department of Production Animal Clinical Sciences, Faculty of Veterinary Medicine, Norwegian University of Life Sciences, Universitetstunet 3, Å s, 1433, Norway.
Preventive veterinary medicine
|February 10, 2026
概括
机器学习模型使用自动奶系统 (AMS) 数据预测奶牛的亚临床乳腺炎 (SCM). 定制模型通过结合测试灵敏度和特异性来提高诊断准确性,增强乳腺健康监测.
科学领域:
- 兽医流行病学 兽医流行病学
- 机器学习 机器学习
- 乳制品科学 乳制品科学
背景情况:
- 自动挤奶系统 (AMS) 产生了大量数据,对乳牛群的健康管理有价值.
- 早期发现亚临床乳腺炎 (SCM) 对于保持乳腺健康和生产力至关重要.
- 现有的SCM诊断指标可以使用先进的分析技术来改进.
研究的目的:
- 开发和评估使用AMS数据预测SCM的新型定制机器学习 (ML) 模型.
- 为了提高准确性,将诊断测试属性 (灵敏度和特异性) 整合到ML模型训练中.
- 用自定义日志概率 (CLL) 函数来评估模型性能,该函数考虑了预测目标的不确定性.
主要方法:
- 利用AMS数据进行SCM预测.
- 模拟感染状况作为细菌学培养 (BC) 和聚合酶链反应 (PCR) 结果的潜在变量.
- 开发定制的ML模型,包括对诊断测试灵敏度和特异性的先前知识.
- 使用定制日志概率 (CLL) 函数评估模型性能,并与模拟和真实数据上的传统指标进行比较.
主要成果:
- 定制的ML模型在真实世界AMS数据上获得了最高的CLL得分.
- 与传统方法相比,模型在模拟数据上显示出明显更好的校准.
- 所有评估的模型都在模拟数据上显示了近乎完美的曲线下面积 (AUC).
- 将测试灵敏度和特异性直接纳入概率函数,提高了不完善目标变量的ML可靠性.
结论:
- 开发的定制ML模型显示出从AMS数据中进行强大的SCM预测的重大前景.
- 该方法增强了用于乳牛群中SCM检测的诊断测试特性.
- 该框架可适应预测其他兽医流行病学结果的不完美测量数据.
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