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改进了基于智能决策系统的树列表结构的适应阶段模糊高效益模式挖掘算法.

Jing Chen1,2, Aijun Liu2, Hongjun Zhang1

  • 1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.

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此摘要是机器生成的。

本研究介绍了一种改进的模糊高效率模式挖掘 (IF-HUPM) 算法,以提高医疗AI决策中的模式解释性. 这种新的方法提高了发现有意义的诊断模式的准确性和效率.

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科学领域:

  • 人工智能的人工智能
  • 数据挖掘 数据挖掘
  • 医疗信息学 医疗信息学

背景情况:

  • 计算机化医疗决策正在与人工智能和大数据一起迅速发展.
  • 高效益模式挖掘 (HUPM) 旨在在医疗数据中找到诊断模式.
  • 现有的HUPM方法往往缺乏用于临床使用的解释性和解释性.

研究的目的:

  • 提出一种新的算法,即改进的模糊高效用模式挖掘 (IF-HUPM),以解决HUPM的解释性限制.
  • 提高医学数据库中发现的诊断模式的意义和可解释性.
  • 在医疗决策中提高模式挖掘的准确性和效率.

主要方法:

  • 采用模糊预处理方法将定量医学数据分割成模糊的间隔,提高数据的模糊性和可解释性.
  • 在IF-HUPM算法中利用模糊树和列表结构来计算模糊的高实用值.
  • 通过整合一阶段和两阶段HUPM算法的特征,开发了一个自适应阶段的Fuzzy HUPM混合框架.

主要成果:

  • IF-HUPM算法在发现高实用性模式方面表现出更高的准确性和效率.
  • 使用IF-HUPM的挖矿过程平均需要更少的计算时间和内存.
  • 实验结果验证了模糊预处理和混合框架的有效性.

结论:

  • 拟议的IF-HUPM算法有效地解决了HUPM在医疗决策中的解释性挑战.
  • IF-HUPM提供了一种更准确,更有效,更易于解释的方法,可以从医疗数据中挖掘有价值的诊断模式.
  • 这项研究通过改进的模式挖掘技术,有助于在医疗保健中推进可解释的AI.