机器学习辅助犬库欣综合征筛查数据集
包含305只犬（152例确诊库欣综合征、153例对照）的常规血液、生化与尿液检验表格数据，用于机器学习辅助筛查建模。
基本信息
资源简介
机器学习辅助犬库欣综合征筛查数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 3 个文件、合计 3.81 MB，主题涵盖 gradient boosting、boosted tree、artificial intelligence、dog、hypercortisolism，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Machine learning-assisted screening for canine Cushing’s syndrome》。配套论文《Machine learning-assisted screening for canine Cushing’s syndrome》发表于 Veterinary Quarterly（2025），DOI：10.1080/01652176.2025.2604643。项目资助方：This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Agriculture and Food Convergence Technologies Program for Research Manpower development funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) under Grant [number: RS-2024-00398561].。源站首次发布：2025-12-31。
【数据内容】
源站原始描述：Cushing’s syndrome (CS) is a common endocrine disorder in dogs that can significantly impair their quality of life. Diagnosis is often challenging because of its variable clinical presentation, making it difficult to identify suitable candidates for further diagnostic tests. This study employed machine learning algorithms to assist in CS diagnosis using routinely available screening diagnostics, including complete blood count, serum chemistry panel, and urinalysis parameters such as urine specif…
中文译文（机器翻译，仅供快速理解，请以原文为准）：库欣综合征（CS）是犬常见的内分泌疾病，可显著损害其生活质量。由于其临床表现多变，诊断往往具有挑战性，因此难以识别适合进一步诊断性检查的候选犬。本研究采用机器学习算法，利用常规可获得的筛查诊断指标辅助CS诊断，包括全血细胞计数、血清生化面板以及尿液分析参数，如尿比重和尿蛋白/肌酐比。数据收集自153只最初怀疑为CS但后来被排除的对照犬和152只确诊CS的犬。在收集数据的80%上训练了一种提升树算法（梯度提升），其余20%留作测试。所开发模型表现出88.5%的准确率[95%置信区间（CI）：80.5–96.5%]、83.3%的敏感性（95% CI：70.7–96.7%）、93.5%的特异性（95% CI：84.9–100%），以及0.912的受试者工作特征曲线下面积（95% CI：0.835–0.988），表明其具有极佳的区分能力。还开发了一个用户友好的图形界面，以促进临床实施，可能提高诊断效率和宠物主人满意度。
【数据结构与技术规格】
文件数 3 个，合计 3.81 MB。tveq_a_2604643_sm7456.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，11.65 KB。tveq_a_2604643_sm3168.tiff：image/tiff，1.20 MB。tveq_a_2604643_sm3167.tiff：image/tiff，2.60 MB。数据集 DOI：10.6084/m9.figshare.30938995.v2。
【主题与分类】
源站学科分类：Medicine、Biotechnology、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Science Policy、Hematology。主题标签：gradient boosting、boosted tree、artificial intelligence、dog、hypercortisolism、non-adrenal illness、polydipsia、polyuria。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Machine_learning-assisted_screening_for_canine_Cushing_s_syndrome/30938995
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Machine learning-assisted screening for canine Cushing’s syndrome. Taylor & Francis. Yoo, Young-Jae; Jeong, Kyungchang; Seo, Hanbit; Nam, Ha-Suk; Chae, Yeon; Yun, Taesik; et al
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Machine learning-assisted screening for canine Cushing’s syndrome
- DOI：10.6084/m9.figshare.30938995.v2
- 发布日期：2025-12-31
- 文件数：3 个
- 文件总体积：3.81 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/Machine_learning-assisted_screening_for_canine_Cushing_s_syndrome/30938995
命令行下载
curl -L -o "tveq_a_2604643_sm7456.docx" "https://ndownloader.figshare.com/files/60574771"
curl -L -o "tveq_a_2604643_sm3168.tiff" "https://ndownloader.figshare.com/files/60574774"
curl -L -o "tveq_a_2604643_sm3167.tiff" "https://ndownloader.figshare.com/files/60574777"
文件清单
tveq_a_2604643_sm7456.docx（11.65 KB）tveq_a_2604643_sm3168.tiff（1.20 MB）tveq_a_2604643_sm3167.tiff（2.60 MB）
文件总体积 3.81 MB（源站 API 实测）。
数据说明
Cushing’s syndrome (CS) is a common endocrine disorder in dogs that can significantly impair their quality of life. Diagnosis is often challenging because of its variable clinical presentation, making it difficult to identify suitable candidates for further diagnostic tests. This study employed machine learning algorithms to assist in CS diagnosis using routinely available screening diagnostics, including complete blood count, serum chemistry panel, and urinalysis parameters such as urine specific gravity and urine protein-to-creatinine ratio. Data were collected from 153 control dogs initially suspected of CS but later excluded and 152 dogs with confirmed CS. A boosted tree algorithm (gradient boosting) was trained on 80% of the collected data, with the remaining 20% reserved for testing. The developed model demonstrated an accuracy of 88.5% [95% confidence interval (CI): 80.5–96.5%], a sensitivity of 83.3% (95% CI: 70.7–96.7%), a specificity of 93.5% (95% CI: 84.9–100%), and an area under the receiver operating characteristic curve of 0.912 (95% CI: 0.835–0.988), indicating excellent discriminatory ability. A user-friendly graphical interface was also developed to facilitate clinical implementation, potentially improving diagnostic efficiency and owner satisfaction.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.30938995.v2（source: https://tandf.figshare.com/articles/dataset/Machine_learning-assisted_screening_for_canine_Cushing_s_syndrome/30938995）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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