摘 要
猫慢性肾病是老年猫常见的慢性疾病,早期诊断对延缓病情进展至关重要。然而,现有的诊断技术在灵敏度和特异性方面存在局限,难以在疾病早期阶段准确识别。本研究旨在优化猫慢性肾病的早期诊断技术,通过整合多种检测手段,提升诊断的准确性和时效性。研究采用了多中心临床数据集,结合血液生化指标、尿液分析和分子生物学技术,开发了一种基于机器学习的综合诊断模型。该模型通过分析猫的血液肌酐、尿素氮、白蛋白等关键指标,并结合尿液中的微量蛋白和细胞学特征,能够显著提高早期CKD的检出率。实验结果表明,优化后的诊断模型在敏感性上较传统方法提升了25%,特异性达到90%以上。此外,研究还发现了一种新的生物标志物——微小RNA-21,其在CKD早期的表达水平显著升高,为疾病的早期预警提供了潜在依据。
关键词:猫慢性肾病;早期诊断;机器学习;生物标志物
OPTIMIZATION OF EARLY DIAGNOSIS TECHNIQUES FOR CHRONIC KIDNEY DISEASE IN CATS
ABSTRACT
Feline chronic kidney disease is a common chronic disease in elderly cats, and early diagnosis is crucial to delay the progression of the disease. However, existing diagnostic techniques have limitations in sensitivity and specificity, making it difficult to accurately identify the disease in its early stages. This study aims to optimize the early diagnosis technology of feline chronic kidney disease, and improve the accuracy and timeliness of diagnosis by integrating multiple detection methods. A multi-center clinical data set was used to develop a comprehensive diagnostic model based on machine learning, combining blood biochemical indicators, urine analysis and molecular biology techniques. This model can significantly improve the early detection rate of CKD by analyzing the key indicators of creatinine, urea nitrogen and albumin in cat blood, combined with the trace protein in urine and cytological characteristics. The experimental results show that the sensitivity of the optimized diagnostic model is 25% higher than that of the traditional method, and the specificity is over 90%. In addition, the study also identified a new biomarker, micrornA-21, whose ex pression level is significantly elevated in the early stage of CKD, providing a potential basis for early warning of the disease.
KEY WORDS:Feline Chronic Kidney Disease; Early Diagnosis; Machine Learning; Biomarker
目 录
摘 要 I
ABSTRACT II
第1章 绪论 2
1.1 研究背景及意义 2
1.2 猫慢性肾病早期诊断技术的研究现状 2
第2章 猫慢性肾病的病理机制与早期诊断指标优化 3
2.1 猫慢性肾病的病理生理学特征分析 3
2.2 现有诊断指标的局限性与优化方向 3
2.3 新型生物标志物的筛选与验证 4
第3章 基于机器学习的猫慢性肾病早期诊断模型构建 5
3.1 数据集构建与预处理方法 5
3.2 机器学习算法在猫慢性肾病诊断中的应用 5
3.3 模型性能评估与优化策略 6
第4章 临床应用中的技术优化与实践探索 7
4.1 实验室检测技术的优化与标准化 7
4.2 临床实践中早期诊断技术的推广与应用挑战 7
4.3 未来发展方向与技术展望 8
第5章 结论 9
参考文献 10
致 谢 11