Early disease detection in veterinary care relies on identifying subclinical abnormalities in asymptomatic animals during wellness visits. This study introduces an algorithm designed to distinguish between wellness and other veterinary visits.The purpose of this study is to validate the use of a visit classification algorithm compared to manual classification of veterinary visits by three board-certified veterinarians. Using a dataset of 11,105 clinical visits from 2012 to 2017 involving 655 animals (85.3% canines and 14.7% felines) across 544 U.S. veterinary establishments, the model was trained using a Gradient Boosting Machine model. Three validators were tasked with classifying 400 visits, including both wellness and other types of visits, selected randomly from the same database used for initial algorithm training, aiming to maintain consistency and relevance between the training and application phases; visit classifications were subsequently categorized into "wellness" or "other" based on majority consensus among validators to assess the algorithm's performance in identifying wellness visits. The algorithm demonstrated a specificity of 0.94 (95% CI: 0.91 to 0.96), implying its accuracy in distinguishing non-wellness visits. The algorithm had a sensitivity of 0.86 (95% CI: 0.80 to 0.92), indicating its ability to correctly identify wellness visits as compared to the annotations provided by veterinary experts. The balanced accuracy, calculated as 0.90 (95% CI: 0.87 to 0.93), further confirms the algorithm's overall effectiveness. The algorithm exhibits strong specificity and sensitivity, ensuring accurate identification of a high proportion of wellness visits. Overall, this algorithm holds promise for advancing research on preventive care's role in subclinical disease identification, but prospective studies are needed for validation.

翻译：兽医护理中的早期疾病检测依赖于在健康检查期间识别无症状动物的亚临床异常。本研究引入了一种旨在区分健康检查与其他兽医就诊的算法。本研究旨在验证该就诊分类算法与三位委员会认证兽医手动分类兽医就诊相比的有效性。使用2012年至2017年间来自544家美国兽医机构的655只动物（85.3%犬类，14.7%猫类）的11,105次临床就诊数据集，通过梯度提升机模型进行训练。三位验证者负责对从初始算法训练所用同一数据库中随机选取的400次就诊（包括健康检查及其他类型就诊）进行分类，旨在保持训练与应用阶段的一致性和相关性；就诊分类随后根据验证者的多数共识归类为“健康检查”或“其他”，以评估算法识别健康检查的性能。该算法显示出0.94的特异性（95% CI: 0.91至0.96），表明其在区分非健康检查就诊方面的准确性。算法灵敏度为0.86（95% CI: 0.80至0.92），表明其与兽医专家提供的标注相比，能正确识别健康检查就诊。计算得出的平衡准确度为0.90（95% CI: 0.87至0.93），进一步证实了算法的整体有效性。该算法展现出较强的特异性和灵敏度，确保能准确识别大部分健康检查就诊。总体而言，该算法有望推动预防性护理在亚临床疾病识别中作用的研究，但需通过前瞻性研究进行验证。