基于机器学习的风险预测模型,用于使用电子健康记录数据来预测犬类myxomatous膜疾病的风险
Yunji Kim1, Jaejin Kim2, Sehoon Kim1
1Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea.
Frontiers in veterinary science
|September 18, 2023
概括
机器学习模型准确地预测了患有肌肉膜病 (MMVD) 的狗的心力衰竭风险. 心声和放射数据是关键预测因素,有助于对犬类心力衰竭预后的临床决策.
科学领域:
- 兽医心脏病学 兽医心脏病学
- 机器学习在医学中的应用
- 狗的健康 狗的健康
背景情况:
- 肌肉膜病 (MMVD) 是狗心力衰竭的主要原因.
- 对犬类MMVD的准确预后评估在临床上具有挑战性.
- 电子健康记录 (EHR) 为预测建模提供了有价值的数据.
研究的目的:
- 开发和评估机器学习模型,用于预测MMVD狗的心力衰竭风险.
- 为了确定这群人群中心力衰竭风险预测的关键临床变量.
主要方法:
- 利用了143只被诊断患有MMVD的狗的EHR数据.
- 应用了四种机器学习算法:随机森林,K-最近邻居,天真贝叶斯和支持向量机器.
- 使用接收器操作特征 (ROC) 曲线和曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 随机森林模型实现了最高的性能,AUC为0.88.8.
- 性能最好的模型显示出出色的预测能力 (AUC ≥0.8).
- 心声学,放射学变量,细胞密集量和呼吸率是重要的预测因素;化物是最具预测力的电解质.
结论:
- 机器学习模型,特别是随机森林,有效地预测MMVD狗的心力衰竭风险.
- 这些模型可以帮助兽医在临床决策和预后估计狗MMVD患者.
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