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个性化心血管危险分层的学习分类树
Learning Classification Trees for Personalized Cardiovascular Risk Stratification |
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| 课程网址: |
http://videolectures.net/nipsworkshops2011_singh_stratification/
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| 主讲教师: |
Anima Singh
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| 开课单位: |
麻省理工学院
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| 开课时间: |
2012-06-23
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| 课程语种: |
英语
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| 中文简介: |
心血管疾病是全世界死亡的主要原因。有许多有效的治疗方法可供选择，但确定高危患者谁最有可能受益于各种疗法是一个尚未解决的问题。风险分层将受益于发展原则性的数据驱动方法，系统地将来自多个风险变量的预后信息合并到一个临床有用的分类树中。在本文中，我们提出了一种分类树归纳算法，并证明它能产生可用于个性化心血管风险分层的树。这样做的一个挑战是医学数据集的高度不平衡。我们的算法在分类树学习的两个关键任务中使用了非对称熵度量：连续变量的离散化和给节点分配一个变量。我们对4219名心血管病患者进行了两项不同的危险分层任务：预测心血管死亡和心肌梗死。对于这两个任务，我们基于分类树的模型优于其他类型的分类树和支持向量机。
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| 课程简介: |
Cardiovascular disease is the leading cause of death worldwide. There are many effective treatments available, but identifying high-risk patients who are most likely to benefit from various therapies is an unsolved problem. Risk stratification would benefit from the development of principled data-driven methods to systematically combine prognostic information from many risk variables into a clinically useful classification tree. In this paper, we present a classification tree induction algorithm, and show that it produces trees that can be used for personalized cardiovascular risk stratification. A challenge in doing this is the high class imbalance in medical datasets. Our algorithm uses non-symmetric entropy measures for two critical tasks in classification tree learning: discretization of continuous variables and assigning a variable to a node. We tested our algorithm on 4219 cardiovascular patients for two different risk stratification tasks: prediction of cardiovascular death and myocardial infarction. For both tasks, our classification tree-based models outperformed other types of classification trees and SVMs.
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| 关 键 词: |
心血管疾病; 分类树; 治疗方法
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| 课程来源: |
视频讲座网
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| 数据采集: |
2020-12-21：yxd
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| 最后编审: |
2021-09-15：zyk
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| 阅读次数: |
30
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