在Covid-19中应用神经相似性测量方法
Rakhal Das1, Anjan Mukherjee1, Binod Chandra Tripathy1
1Department of Mathematics, Tripura University Agartala, Tripura, 799022 India.
概括
本研究介绍了一种使用中性质相似度测量方法来预测COVID-19测试结果的决策模型. 该模型有助于识别可能对病毒呈阳性或阴性测试的患者.
科学领域:
- 医疗信息学 医疗信息学
- 决策科学 决策科学 决策科学
- 计算智能是一种计算智能.
背景情况:
- 全球COVID-19大流行需要先进的诊断和预测工具.
- 准确的患者分层对于有效的资源配置和治疗策略至关重要.
- 现有的决策模型可能无法完全捕捉医疗诊断中固有的不确定性.
研究的目的:
- 开发和介绍一种用于预测COVID-19患者结果的新型决策模型.
- 为了提高预测准确性,利用中性学相似性测量.
- 根据现有数据,区分COVID-19阳性和阴性检测的患者.
主要方法:
- 中性相似度测量理论的应用.
- 集成距离功能用于数据分析.
- 使用C编程实现的预测模型的开发.
- 分析COVID-19测试结果用于决策.
主要成果:
- 开发的模型证明了预测COVID-19测试结果的能力.
- 中性相似性测量为处理患者数据中的不确定性提供了一个强大的框架.
- 通过C编程实现,可对疑似病例的结果进行高效的计算.
结论:
- 拟议的基于中性学相似性的决策模型为COVID-19患者预测提供了一个有希望的方法.
- 这种方法可以帮助医疗保健专业人员做出有关患者测试和管理的明智决策.
- 进一步的研究可以探索在这个框架内集成更复杂的数据类型.
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