Heinen Lilli, Larson Robert L, White Brad J
Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66502, USA.
Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66502, USA.
Animals (Basel). 2025 Aug 23;15(17):2481. doi: 10.3390/ani15172481.
Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data types utilized to train models. This narrative review aims to describe predictive model performance in greater detail across various disease outcomes, input data types, and algorithms with a specific focus on accuracy, sensitivity, specificity, and positive and negative predictive values. A secondary goal is to address important areas for consideration for future work in the beef cattle sector. In total, 19 articles were included. Broad categories of disease were covered, including respiratory disease, bovine tuberculosis, and others. Various input data types were reported, including demographic data, images, and laboratory test results, among others. Several algorithms were utilized, including neural networks, linear models, and others. Accuracy, sensitivity, and specificity values ranged widely across disease outcome and algorithm categories. Negative predictive values were greater than positive predictive values for most disease outcomes. This review highlights the importance of utilizing several performance metrics and concludes that future work should address prevalence of outcomes and class-imbalanced data.
预测模型利用历史数据来预测未来事件，并且可以应用于各种各样的任务。需要对牛类文献进行更广泛的评估，以便更好地了解预测模型在应对各种健康挑战时的性能，并了解用于训练模型的数据类型。本叙述性综述旨在更详细地描述预测模型在各种疾病结局、输入数据类型和算法方面的性能，特别关注准确性、敏感性、特异性以及阳性和阴性预测值。第二个目标是探讨肉牛领域未来工作需要考虑的重要领域。总共纳入了19篇文章。涵盖了广泛的疾病类别，包括呼吸道疾病、牛结核病等。报告了各种输入数据类型，包括人口统计学数据、图像和实验室检测结果等。使用了几种算法，包括神经网络、线性模型等。准确性、敏感性和特异性值在疾病结局和算法类别之间差异很大。对于大多数疾病结局，阴性预测值大于阳性预测值。本综述强调了使用多种性能指标的重要性，并得出结论，未来的工作应解决结局的患病率和类别不平衡数据的问题。
