一种新的基于云计算的方法来构建医疗保健服务,使用深度强化学习和卡尔曼过
Chongzhou Zhong1, Mehdi Darbandi2, Mohammad Nassr3
1School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
本研究介绍了一种使用深度强化学习 (Deep RL) 和卡尔曼过来优化基于云的医疗保健服务的新方法. 该方法改善了服务选择和组成,降低了成本和响应时间.
科学领域:
- 医疗信息学 医疗信息学
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
背景情况:
- 云计算为医疗保健提供了可扩展和按需的服务,有利于医疗保健.
- 选择和构建基于云的医疗保健服务至关重要,但面临诸如高能耗和响应时间等挑战.
研究的目的:
- 开发一种新的分层方法来选择和评估基于云计算的医疗保健服务.
- 在医疗保健服务组成中解决能源消耗,成本和响应时间的问题.
主要方法:
- 一种新的分层方法,整合了深度强化学习 (Deep RL),卡尔曼过和重复训练.
- 评估医疗保健服务选择和组成解决方案.
主要成果:
- 提出的方法在可用性,可靠性,能耗和响应时间方面取得了可接受的结果.
- 与云计算医疗保健服务优化现有方法相比,证明了改进.
结论:
- 开发的Deep RL和Kalman基于过的方法为优化云医疗服务提供了有效的解决方案.
- 这种方法通过改进选择和组成来提高医疗保健服务的效率和可行性.
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