A biological continuum based approach for efficient clinical classification.
A biological continuum based approach for efficient clinical classification.
复制标题
基于生物连续体的有效临床分类方法。
DOI：
10.1016/j.jbi.2013.09.002
复制
发表时间：
2014
影响因子：
4.5
通讯作者：
Tay D
中科院分区：
文献类型：
--
作者：
Tay D
临床特征选择问题是选择和识别一个有用的临床特征子集，以促进准确的临床诊断的任务。这在临床环境中是具有实用价值的重要任务，因为每个临床测试与不同的财务成本、诊断价值和获得测量的风险相关联。此外，随着新临床特征的不断引入，重复特征选择任务的需要可能非常耗时。因此，为了解决这个问题，我们提出了一种新的特征选择技术，用于诊断心肌梗死-在许多高收入国家的发病率和死亡率的主要原因之一。该方法采用了生物连续统的概念框架、遗传算法进行特征选择的优化能力和支持向量机的分类能力。共同构建了一个临床风险因素网络，称为基于生物连续体的病因学网络（BCEN）。使用心血管心脏研究（CHS）数据集进行评估所提出的方法。结果表明，一个显着的加速4.73倍，可以实现MI分类模型的发展。这种方法的主要优点是提供了一个可重用的（功能子集）范例，有效地开发最新的和有效的临床分类模型。
Clinical feature selection problem is the task of selecting and identifying a subset of informative clinical features that are useful for promoting accurate clinical diagnosis. This is a significant task of pragmatic value in the clinical settings as each clinical test is associated with a different financial cost, diagnostic value, and risk for obtaining the measurement. Moreover, with continual introduction of new clinical features, the need to repeat the feature selection task can be very time consuming. Therefore to address this issue, we propose a novel feature selection technique for diagnosis of myocardial infarction – one of the leading causes of morbidity and mortality in many high-income countries. This method adopts the conceptual framework of biological continuum, the optimization capability of genetic algorithm for performing feature selection and the classification ability of support vector machine. Together, a network of clinical risk factors, called the biological continuum based etiological network (BCEN), was constructed. Evaluation of the proposed methods was carried out using the cardiovascular heart study (CHS) dataset. Results demonstrate a significant speedup of 4.73-fold can be achieved for the development of MI classification model. The key advantage of this methodology is the provision of a reusable (feature subset) paradigm for efficient development of up-to-date and efficacious clinical classification models.