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Case-based reasoning in Neurological Domain

机译:神经领域中基于案例的推理

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摘要

Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) methodology like rule-based reasoning, neural networks and genetic algorithms. CBR techniques used to dynamically knowledge to discover from previous experiences and advance filtering of variables was the best predictor in comparison to other machine learning methods. This paper presents a Neurology Diagnosis System (NDS) to CBR approach. This system assists to health assistants in the absence of the expert doctors. The work features a compositional adaptation approach, whereby relevant health information elements from the solution component of multiple similar past cases are carefully selected and systematically combined to yield a new personalized health information package. The implementation is a web application developed in the Java programming language using the Spring MVC framework and My-SQL as the database system.
机译:基于案例的推理(CBR)是一种人工智能(AI)方法,例如基于规则的推理,神经网络和遗传算法。与其他机器学习方法相比,用于动态了解知识以从先前的经验中发现并预先进行变量过滤的CBR技术是最佳的预测指标。本文提出了一种神经病诊断系统(NDS)的CBR方法。该系统可在专家医生不在的情况下为健康助手提供帮助。这项工作采用了成分适应方法,通过这种方法,可以仔细选择多个过去相似案例的解决方案组成部分中的相关健康信息元素,并系统地进行组合,以生成新的个性化健康信息包。该实现是使用Spring MVC框架和My-SQL作为数据库系统以Java编程语言开发的Web应用程序。

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