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首页> 外文期刊>International Journal of Hybrid Intelligent Systems >Knowledge discovery in repeated very short serial measurements with a blocking factor. Application to a psychiatric domain
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Knowledge discovery in repeated very short serial measurements with a blocking factor. Application to a psychiatric domain

机译：在重复的非常短的串行测量中，使用阻塞因子进行知识发现。在精神病学领域的应用

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摘要

A new hybrid methodology for Knowledge Discovery in Serial Measurement (KDSM) and the results of applying it to psychiatry are presented in this paper. In the application domain where serial measurements are repeated and very short (i.e. very few parameters), traditional measuremethods for series analysis are inappropriate. Moreover, some information is non-serial but is closely connected to serial measurements. For this reason, common statistical analysis (time series analysis, multivariate data analysis ...) and artificial intelligence techniques (knowledge based methods, inductive learning) used independently provide often poor results because of the characteristics above and it is necessary a suitable way of analyzing these situations. KDSM is built as an hybrid methodology, specially designed to obtain knowledge from repeated very short serial measurement, in order to overcome the limitations of Artificial Intelligence or Statistics techniques. Novel knowledge about electroconvulsive therapy behavior was obtained once KDSM was applied to this specific domain. Thus, KDSM gives a possible solution to a knowledge problem.
机译：本文介绍了一种用于序列测量中的知识发现（KDSM）的新的混合方法，并将其应用于精神病学的结果。在重复进行串行测量且时间很短（即参数很少）的应用领域中，用于序列分析的传统测量方法是不合适的。此外，某些信息不是串行的，但与串行测量紧密相关。因此，由于上述特性，通常单独使用的常用统计分析（时间序列分析，多元数据分析...）和人工智能技术（基于知识的方法，归纳学习）通常提供较差的结果，因此有必要采用一种合适的方法分析这些情况。 KDSM是一种混合方法，专门设计用于从重复的非常短的串行测量中获取知识，以克服人工智能或统计技术的局限性。一旦将KDSM应用于该特定领域，就可以获得有关电惊厥治疗行为的新知识。因此，KDSM为知识问题提供了可能的解决方案。

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