一个基于医疗物联网数据的癌症诊断变压器模型,用于预测护理系统中的临床测量
Panpan Li1, Yan Lv2, Haiyan Shang3
1Department of Pulmonary and Critical Care Medicine, the Sixth Medical Center of PLA General Hospital, Beijing 100048, China.
BioImpacts : BI
|April 21, 2025
概括
这项研究引入了一种新的变压器模型,用于使用物联网 (IoT) 数据进行疾病诊断. 该方法有效地提取和选择相关特征,以提高预测医疗保健的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 生物医学信号处理
背景情况:
- 信息和通信技术 (ICT) 和物联网 (IoT) 正在通过实现先进的数据分析来改善患者护理来改变医疗保健.
- 预测性护理系统对于早期诊断和治疗疾病,包括癌症至关重要.
研究的目的:
- 提出一种新的疾病诊断方法,利用医疗物联网数据和变压器模型.
- 通过先进的特征提取和选择技术,提高预测护理系统的准确性.
主要方法:
- 利用带有卷积神经网络 (CNN) 编码器的变压器网络从物联网图像中提取特征.
- 采用了带有注意力机制的解码层,用于精致的特征表示.
- 应用了哈里斯·霍克优化算法来从高维,冗余数据集中选择相关特征.
主要成果:
- 拟议的基于变压器的方法在5类数据集上进行了评估.
- 使用各种分类器评估性能,结果以表格和图表形式呈现.
- 与现有方法相比,实验结果显示了可接受的性能.
结论:
- 开发的方法展示了变压器模型与物联网数据结合用于疾病诊断的潜力.
- 使用优化算法的特征选择在处理高维度医疗数据方面是有效的.
- 这项研究有助于推进人工智能驱动的预测医疗保健解决方案.
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