A machine learning and blockchain based secure and cost-effective framework for minor medical consultations

【Author】 Hassija, Vikas; Ratnakumar, Rahul; Chamola, Vinay; Agarwal, Soumya; Mehra, Aryan; Kanhere, Salil S.; Huynh Thi Thanh Binh

【Source】SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS

【影响因子】4.923

【Abstract】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.

【Keywords】Machine learning; NLP; Naive Bayes; Logistic regression; Minor consultations; Ethereum; Blockchain

【发表时间】2022 SEP

【收录时间】2022-07-17

【文献类型】理论性文章

【主题类别】

区块链应用-实体经济-医疗领域