在亚洲人群中,基于机器学习的慢性全闭症风险因素分析
Yuchen Shi1, Zichao Cheng1, Wen Jian1
1Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, and Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
The Journal of international medical research
|October 11, 2023
概括
机器学习模型可以预测慢性全塞 (CTO),一种冠状动脉疾病的形式. 关键预测因素包括性别,中性粒细胞百分比,肌素和大脑天然尿素 (BNP).
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 慢性全塞 (CTO) 是冠状动脉疾病 (CAD) 管理的一个重大挑战.
- 皮肤冠状动脉干预通常需要CTO,但对预测模型的研究是有限的.
- 在血管造影之前识别CTO患者可以优化治疗策略.
研究的目的:
- 根据临床特征开发和验证用于预测CTO的机器学习模型.
- 确定与CTO开发相关的关键临床风险因素.
主要方法:
- 对1473名CAD患者 (317名CTO,1156名非CTO) 的数据进行了回顾性分析.
- 使用部分最小平方差分分析 (PLS-DA),随机森林 (RF) 和支持矢量机器 (SVM) 开发预测模型.
- 使用接收器操作特征 (ROC) 曲线分析评估模型性能.
主要成果:
- 这三种模型都显示了可比的预测性能 (ROC值约为0.70).
- PLS-DA模型确定了10个变量,RF确定了42个,SVM确定了20个.
- 在所有模型中,性别,中性粒细胞百分比,肌素和脑性尿素 (BNP) 一致地被确定.
结论:
- 机器学习模型,特别是PLS-DA,在冠状动脉血管学之前预测CTO方面表现有前途.
- 临床因素,如性别,中性粒细胞百分比,肌素和BNP是CTO的潜在重要的危险因素.
关键词:
慢性完全闭塞 慢性完全闭塞大脑 natriuretic 的.冠状动脉动脉图 (Coronary Angiography) 是一种冠状动脉动脉图.冠状动脉疾病是一种冠状动脉疾病.肌氨酸是什么意思 肌氨酸是什么意思超声心电图 (Echocardiography) 是一种心声回声仪.机器学习是机器学习.预测 预测 预测 预测风险因素的风险因素是什么更多相关视频
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