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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: |
2012-01-23
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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，但确定最有可能从各种治疗中受益的高风险患者是一个尚未解决的问题。风险分层将受益于原型数据驱动方法的发展，以系统地将来自许多风险变量的预后信息组合到临床上有用的分类树中。在本文中，我们提出了一种分类树归纳算法，并表明它产生的树木可以用于个性化的心血管风险分层。这样做的挑战是医疗数据集中的高级不平衡。我们的算法使用非对称熵测量来分类树学习中的两个临界任务：连续变量的离散化和为节点分配变量。我们在4219名心血管病人身上测试了我们的算法，用于两种不同的风险分层任务：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执行其他类型的分类树和SVM。
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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; 风险分层; 分类树
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-07：lxf
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| 阅读次数: |
54
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