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Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

机译:实时临床决策支持系统中流分类器的评估:糖尿病治疗中血糖预测的案例研究

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摘要

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.
机译:早些时候,提出并发布了带有数据流挖掘的实时临床决策支持系统(rt-CDSS)的概念设计。引入了可以分析医疗数据流并可以进行实时预测的新系统。该系统基于称为VFDT的流挖掘算法。 VFDT扩展了使用指针的能力,以允许决策树记住叶节点和历史记录之间的映射关系。本文是rt-CDSS设计的续篇,研究了几种流行的机器学习算法,它们适合用作rt-CDSS分类器的候选对象。分类器本质上需要将输入到系统的事件准确地映射到几种预定义的评估类别之一中,以便rt-CDSS可以跟进推荐给临床医生的处方疗法。对于像rt-CDSS这样的实时系统,主要的技术挑战在于分类器快速,可靠地处理,分析和分类动态输入数据的能力。进行实验比较。本文以糖尿病治疗为例,为选择流挖掘分类器并将其嵌入rt-CDSS做出了贡献。

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