一个现代的人工智能框架,整合了深度归算,合成数据平衡和可解释的建模,用于马中生存预测
Zeynep Banu Ozger1, Pınar Cihan2, Isa Ozaydin3
1Department of Computer Engineering, Faculty of Engineering and Architecture, Kahramanmaras Sutcu Imam University, Kahramanmaraş, Türkiye.
这项研究开发了一种先进的人工智能 (AI) 模型,以准确预测患有的马的生存率. 性能最好的AI管道实现了高准确度,识别了改善马结肠发作预后的关键临床因素.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 为兽医的临床决策支持提供了变革性的潜力.
- 尽管马医药具有经济和临床意义,但它仍未充分利用人工智能.
- 准确地预测患有的马的生存率,对于及时治疗和更好的结果至关重要.
研究的目的:
- 开发和评估人工智能模型,用于预测马匹结肠病例的生存结果.
- 为了完成这个任务,将传统的机器学习算法与深度学习架构进行比较.
- 确定影响结肠生存预测的关键临床变量.
主要方法:
- 将传统的机器学习 (XGBoost,LightGBM,CatBoost) 与深度学习 (TabNet,FT_Transformer,NODE) 结合起来.
- 使用深度学习归算 (GAIN,MIDAS) 用于缺少的数据和生成模型 (CTGAN,TVAE) 用于类不平衡.
- 使用可解释AI (XAI) 与SHapley添加式解释 (SHAP) 进行模型解释.
主要成果:
- TVAE-GAIN-OneHot-LightGBM管道实现了最高的性能,其AUC为0.928.
- 这种人工智能管道表现优于传统的统计和机器学习基线.
- SHAP分析确定了总蛋白质,腹部外观,粘膜,包装细胞体积和四肢温度作为关键预测因素.
结论:
- 数据完整性,模型优化和XAI集成提高了兽医AI模型的可靠性.
- 这项研究提出了一种新的,可解释的框架,用于马类结肠炎的预后.
- 这种方法有利于在临床兽医实践中更广泛地采用人工智能.
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