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TPVNet:一个基于域的基于图形的框架,用于医疗保健中可靠的多变量生理时间序列分类
Xinyue Ren1, Yuxuan Xiu2, Ting Chen3
1Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, China.
TPVNet 通过使用新型图形框架,增强了医疗保健的多变量生理时间序列分类. 它提高了准确性和稳定性,同时保护了医疗物联网应用程序中的数据隐私.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 多变量生理时间序列分类对于医疗事物互联网 (IoMT) 中的医疗决策支持至关重要.
- 现有的方法面临着杂的挑战,非静止的医疗信号和隐私问题.
- 需要对IoMT建立强大且保护隐私的分类框架.
研究的目的:
- 引入TPVNet,一个新的基于图形的框架,用于多变量生理时间序列的分类.
- 为了提高IoMT应用中的分类准确性,稳定性和隐私保护.
- 解决现有方法在处理杂和非静止医疗数据方面的局限性.
主要方法:
- TPVNet使用一个时间增强的有限可穿透可见度图 (TPVG) 来将时间序列转换为具有丰富时间特征的不可逆转的图形表示.
- 图形同态网络 (GIN) 用于特征学习.
- 一个与临床工作流程一致的道智能投票策略,增强了决策的稳定性.
主要成果:
- 在7个公共生理学数据集中,TPVNet在6个数据集中获得了最高的F1分数.
- 在数据稀缺的场景中,它的表现显著超过了基线,将心脏动 (AF) 的分类准确度提高了22.0%.
- 废弃性研究证实累计准确度增加了12.7%,证明了TPVG和投票机制的有效性. 此外,TPVNet在标准偏差较低的情况下表现出卓越的稳定性.
结论:
- TPVNet为多变量生理时间序列分类提供了一个隐私意识,准确和稳定的解决方案.
- 该框架整合了基于领域的图形构建和临床决策融合,用于现实世界IoMT应用.
- TPVNet弥合了先进的算法设计和实际医疗保健需求之间的差距.
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