使用含水疗数据的模糊推理系统预测浴后体温
Feng Han1, Minghui Tang2,3, Ziheng Zhang1
1Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-Ku, Sapporo 060-8638, Hokkaido, Japan.
Healthcare (Basel, Switzerland)
|May 14, 2025
概括
在水疗后准确预测体温对于安全至关重要. 一个进化模糊推理系统 (EVOFIS) 显示出预测深层体温的前景,提高了患者的护理.
科学领域:
- 身体生理学 身体生理学
- 生物医学工程 生物医学工程
- 计算智能是一种计算智能.
背景情况:
- 水疗利用水的物理性质来获得治疗效益,影响生理反应.
- 体温调节是水疗的关键,影响血液循环,肌肉放松和新陈代谢.
- 在水疗中不适当的温度控制存在风险,特别是在脆弱人群中.
研究的目的:
- 开发和比较用于预测水疗后体温的计算模型.
- 评估模糊推理系统 (FIS) 与用于温度预测的机器学习模型的有效性.
- 通过准确的温度预测,提高水疗的安全性和个性化.
主要方法:
- 使用了自适应的神经模糊推理系统,进化模糊推理系统 (EVOFIS) 和增强的Takagi-Sugeno模糊系统.
- 将FIS模型与随机森林和支矢量机器模型进行比较.
- 雇佣了与水疗相关的数据集,用于模型培训和验证.
主要成果:
- 进化模糊推断系统 (EVOFIS) 在预测浴后体温方面表现出卓越的表现.
- 埃沃菲斯特别擅长预测深层体温,这是生理调节的关键指标.
- 基于FIS的模型显示了非侵入性温度预测的潜力.
结论:
- 准确的深度温度预测允许在水疗期间主动管理高温症风险.
- EVOFIS和其他FIS模型为有风险的个人提供了更安全的水疗实践的途径.
- 这些发现支持将先进的计算模型集成到个性化和更安全的水疗应用中.
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