基于云的个人健康记录(PHR)和个性化印度健康网络

Nikitha Tripuraneni, Rajashekhar Balla, Trisha Sai Paladugu, Fathimabi Shaik
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引用次数: 0

摘要

基于云的PHR和个性化印度健康网络是一个社交网络和用户存储库,其目标是连接用户并将有类似医疗问题的人聚集在一起。PIHN帮助建立一个患者网络,让他们提供建议并分享经验。此外,PIHN使患者能够存储他们过去的医疗记录,这样他们就可以随时访问,而不必随身携带物理副本。基于云的PHR对用户是可访问的,并且具有恢复丢失副本的能力。个性化的印度健康网络是专门为印度人建立的。此外,PIHN有一个自我跟踪系统,使用户能够根据自己的日常健康状况监测自己的健康状况。PIHN有一个推荐系统,可以将具有相似个人资料的人联系起来。通过交流经验,该系统使用户能够咨询医生并获得更好的护理。为了提供推荐,我们使用了一种合适的方法来衡量用户的相似度。彼此相似的病人形成了一个交流网络。用户的隐私得到保护,未经用户允许,用户的数据不会被泄露。Django框架用于前端,Firebase用作后端存储。在自我跟踪系统中,用户的数据以图形形式可视化,以便快速方便地了解他们的健康状况。患者对患者的推荐使用基于用户的协同过滤算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud Based Personal Health Record (PHR) and Personalized Indian Health Network
Cloud Based PHR & Personalized Indian Health Network is a social network and user repository with the goal of connecting users and bringing together people with similar medical issues. PIHN assists in creating a network of patients who may offer suggestions and share their experiences. Additionally, PIHN makes it possible for patients to store their past medical records so they can be accessible at any time without having to carry a physical copy with them everywhere. Cloud Based PHR is accessible to the user and has the ability to recover lost copies. Personalized Indian Health Network is especially made for Indians. Also, the PIHN has a self-tracking system that enables users to monitor their health status based on their day-to-day health conditions. PIHN has a recommender system that connects people with similar profiles. By exchanging experiences, this system enables users to consult a doctor and receive better care. To provide recommendations, an appropriate method is used to measure user similarities. Patients who are similar to one another form a network to communicate. Users’ privacy is maintained, and their data is never disclosed without their permission. The Django framework is used for the frontend and Firebase is used as the backend storage. In Self-tracking system, the user’s data is visualized in graphs for quick and easy understanding of their health status. Patient-to-patient recommendations are made using user-based collaborative filtering algorithms.
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