Szlosek Donald, Coyne Michael, Riggott Julia, Knight Kevin, McCrann D J, Kincaid Dave
IDEXX Laboratories, Inc., Westbrook, ME, United States.
Front Vet Sci. 2024 Aug 30;11:1348162. doi: 10.3389/fvets.2024.1348162. eCollection 2024.
Early disease detection in veterinary care relies on identifying subclinical abnormalities in asymptomatic animals during wellness visits. This study introduces a model designed to distinguish between wellness and other types of veterinary visits.
The purpose of this study is to validate the use of a visit classification model compared to manual classification of veterinary visits by three board-certified veterinarians.
The algorithm was initially trained using a Gradient Boosting Machine model with a dataset of 11,105 clinical visits from 2012 to 2017 involving 655 animals (85.3% dogs and 14.7% cats) across 544 U.S. veterinary practices. 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 model 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 model's performance in identifying wellness visits.
The model demonstrated a specificity of 0.94 (95% CI: 0.91 to 0.96), implying its accuracy in distinguishing non-wellness visits. The model 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 model's overall effectiveness.
The model exhibits high specificity and sensitivity, ensuring accurate identification of a high proportion of wellness visits. Overall, this model holds promise for advancing research on preventive care's role in subclinical disease identification, but prospective studies are needed for validation.
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情况。本研究引入了一个旨在区分健康检查和其他类型兽医就诊的模型。
本研究的目的是验证与三位获得董事会认证的兽医对兽医就诊进行手动分类相比，就诊分类模型的使用情况。
该算法最初使用梯度提升机模型进行训练，数据集来自2012年至2017年的11,105次临床就诊，涉及美国544家兽医诊所的655只动物（85.3%为狗，14.7%为猫）。三位验证者负责对400次就诊进行分类，包括健康检查和其他类型的就诊，这些就诊是从用于初始模型训练的同一数据库中随机选择的，目的是保持训练阶段和应用阶段之间的一致性和相关性；随后，根据验证者之间的多数共识，将就诊分类为“健康检查”或“其他”，以评估该模型在识别健康检查就诊方面的性能。
该模型的特异性为0.94（95%置信区间：0.91至0.96），这意味着它在区分非健康检查就诊方面的准确性。该模型的灵敏度为0.86（95%置信区间：0.80至0.92），表明与兽医专家提供的注释相比，它能够正确识别健康检查就诊。计算得出的平衡准确率为0.90（95%置信区间：0.87至0.93），进一步证实了该模型的整体有效性。
该模型具有高特异性和灵敏度，可确保准确识别大部分健康检查就诊。总体而言，该模型有望推动关于预防保健在亚临床疾病识别中作用的研究，但需要进行前瞻性研究以进行验证。
