Hassija Vikas, Ratnakumar Rahul, Chamola Vinay, Agarwal Soumya, Mehra Aryan, Kanhere Salil S, Binh Huynh Thi Thanh
Department of CS and IT, Jaypee Institute of Information Technology, Noida, 201304, India.
Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, India.
Sustain Comput. 2022 Sep;35:100651. doi: 10.1016/j.suscom.2021.100651. Epub 2021 Dec 27.
With the ever-increasing awareness among people regarding their health, visiting a doctor has become quite common. However, with the onset of the COVID-19 pandemic, home-based consultations are gaining popularity. Nevertheless, the worries over privacy and the lack of willingness to assist patients by the medical professionals in the online consultation process have made current models ineffective. In this paper, we present an advanced protected blockchain-based consultation model for minor medical conditions. Our model not only ensures users' privacy but by incorporating a calculation model, it also offers an opportunity for consulting end-users to voluntarily take part in the consultation process. Our work proposes a smart contract based on machine learning to be implemented for the prediction of a score of a professional who consults based on various prioritized parameters. This is done by using word2vec and TF-IDF weighting to classify the question and cosine similarity scores for detailed orientation analysis. Based on this score, the patient is charged, and simultaneously, the responder is awarded ether. An incentivized method leads to more accessible healthcare while reducing the cost itself.
随着人们对自身健康的意识不断提高，去看医生变得相当普遍。然而，随着新冠疫情的爆发，居家问诊越来越受欢迎。尽管如此，对隐私的担忧以及医疗专业人员在在线问诊过程中缺乏帮助患者的意愿，使得当前的模式效果不佳。在本文中，我们提出了一种先进的基于受保护区块链的轻症医疗问诊模式。我们的模式不仅确保用户隐私，而且通过纳入一种计算模型，还为问诊终端用户提供了自愿参与问诊过程的机会。我们的工作提出了一种基于机器学习的智能合约，用于根据各种优先参数对专业问诊人员的评分进行预测。这是通过使用词向量和TF-IDF加权来对问题进行分类，并使用余弦相似度分数进行详细的定向分析来实现的。基于这个分数，向患者收费，同时，向应答者奖励以太币。一种激励方法在降低成本的同时，使医疗服务更容易获得。
