设计一个医疗信息诊断平台与物联网集成
Hejian Liu1, Xin Guan2, Rong Bai3
1School of Education, Teachers College, Guangzhou University, Guangzhou, 510006, China.
Heliyon
|February 8, 2024
概括
这项研究利用先进的群体和进化算法优化了医疗诊断平台,提高了效率和稳定性. 改进的算法在健康值和医疗保健应用数据处理方面表现更好.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 当前的医疗信息诊断平台利用聊天生成预训练变压器 (ChatGPT) 和物联网 (IoT) 技术.
- 医疗保健的运营效率需要先进的诊断工具.
- 群体和进化算法为优化这些平台提供了潜力.
研究的目的:
- 调查群体和进化算法的应用,以增强医疗信息诊断平台.
- 使用反向学习和高斯函数优化群集算法.
- 验证拟议的优化算法的有效性和稳定性.
主要方法:
- 分析使用ChatGPT和物联网的当前医疗诊断平台开发.
- 探索医疗诊断中的群体和进化算法的优缺点.
- 通过反向学习和高斯函数优化群算法.
- 通过水平比较实验和稳定性测试进行验证.
主要成果:
- 优化的算法在最低,平均和最大的健身值中表现出卓越的性能.
- 在10*10服务器配置中的数据预处理进一步改善了算法适应性.
- 优化的算法实现了3.56的最低健身值,比未排序的基线提高3%.
- 优化的算法表现出增强的稳定性,特别是在与排序算法相结合时.
结论:
- 优化的群体和进化算法为医疗信息诊断提供了新的视角.
- 提出的方法为医疗信息处理中的实际应用提供了有效的技术支持.
- 这项研究有助于通过人工智能驱动的诊断来提高医疗保健行业的运营效率.
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