开发一种可解释的机器学习计算模型,用于预测在马中进行管切除术后出现的严重并发症
Panagiota Tyrnenopoulou1, Dimitris Kalatzis2, Yiannis Kiouvrekis2,3,4
1Veterinary Faculty, University of Thessaly, 43100 Karditsa, Greece.
Animals : an open access journal from MDPI
|February 13, 2026
概括
监督的机器学习模型可以预测马科切除术后的严重并发症. 年龄和手术技术是关键预测因素,有助于在马术前护理中的临床决策.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 医疗保健中的机器学习
- 手术预测结果的预测
背景情况:
- 马切除术是马中常见的一种手术.
- 严重的术后并发症可能会出现,影响马的福利.
- 需要预测工具来改善外科手术期间的管理.
研究的目的:
- 开发和评估监督机器学习模型,用于预测马科切除术后的严重并发症.
- 确定影响并发症发展的关键因素.
- 评估计算模型在临床决策中的实用性.
主要方法:
- 利用了612例马匹切除术病例的数据集.
- 采用了三个监督机器学习算法:逻辑回归,随机森林和梯度提升.
- 使用准确性,精度和回忆等歧视措施评估模型性能.
主要成果:
- 逻辑回归表现出最好的预测性能,准确度为 (0.9134),精度为 (0.8391),回忆率为 (0.9133).
- 马的年龄和手术技术被确定为所有模型中对并发症预测最有影响力的变量.
- 严重的并发症发生在8.5%的病例中,包括肠,持续的马般的行为,内脏剥离,管炎,出血和阴囊感染.
结论:
- 监督机器学习模型,特别是物流回归,显示出在预测马科整形术后严重并发症的巨大潜力.
- 马的年龄和手术技术是风险评估中需要考虑的关键因素.
- 计算模型可以作为有价值的辅助工具,以支持兽医在马的术后护理中的临床决策.
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