一个基于机器学习和区块链的安全和具有成本效益的框架,用于小型医疗咨询
Vikas Hassija1, Rahul Ratnakumar2, Vinay Chamola3
1Department of CS and IT, Jaypee Institute of Information Technology, Noida, 201304, India.
Sustainable computing : informatics and systems
|July 31, 2023
概括
这项研究引入了一种基于区块链的安全模型,用于在线医疗咨询,增强患者隐私并激励医疗保健专业人员. 这种创新方法旨在提高可访问性,并降低轻微健康状况的成本.
科学领域:
- 医疗信息学 医疗信息学
- 区块链技术 区块链技术
- 人工智能的人工智能
背景情况:
- 越来越多的健康意识增加了医生访问.
- 由于COVID-19的流行,在家就诊的采用加速了.
- 当前的在线模式面临着隐私和专业参与的挑战.
研究的目的:
- 介绍一个先进的,受保护的基于区块链的咨询模式,用于轻微的医疗条件.
- 解决隐私问题,加强专业人士参与在线咨询.
- 开发一个鼓励系统,提供可访问和成本效益的医疗保健.
主要方法:
- 利用基于区块链的框架进行安全的咨询.
- 纳入了用于专业评分的机器学习模型.
- 使用word2vec,TF-IDF和等号相似性来进行问题分类和分析.
主要成果:
- 拟议的模型确保在线咨询期间的用户隐私.
- 开发了一个智能合约,根据参数预测专业成绩.
- 一个激励机制奖励专业人员,并根据咨询得分向患者收费.
结论:
- 基于区块链的模型增强了远程医疗中的隐私和安全性.
- 机器学习集成优化了专业匹配和激励.
- 这种方法促进了对轻微疾病的更容易获得和负担得起的医疗保健解决方案.
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