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基于机器学习的心力衰竭发生风险预测模型的研究进展
作者:徐倩1  徐翠荣1 2  蔡雪2  孙惠1  郑月月1 2 
单位:1. 东南大学医学院 护理系, 江苏 南京 210009;
2. 东南大学附属中大医院 护理部, 江苏 南京 210009
关键词:机器学习 心力衰竭 风险因素 预测模型 综述 
分类号:R248.1
出版年·卷·期(页码):2024·52·第五期(807-815)
摘要:

通过搜索并精读国内外相关研究,对心力衰竭相关机器学习模型进行系统分析,并从Logistic回归、随机森林、梯度提升树、支持向量机(SVM)、极致梯度提升树(XGBoost)等5个模型分析心力衰竭模型应用现状及优势。本文作者综述了人工智能的发展进程及将机器学习应用于心力衰竭发生发展预测的研究现状,以帮助医务人员及早识别高风险人群和采取干预措施提供理论参考。

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