[论文解读] E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine

Doaa Mohey El-Din, Aboul Ella Hassanein+2 more|arXiv (Cornell University)|May 5, 2020

IoT and Edge/Fog Computing参考文献 26被引用 11

一句话总结

E-Quarantine 提出了一种基于物联网（IoT）和人工智能（AI）的远程健康监测系统，用于监测新冠肺炎患者，以减轻医院负担并保护医护人员。该系统通过可穿戴传感器持续收集体温、呼吸频率、心率、血压等生命体征，并支持实时远程监测与临床决策支持，从而在降低感染风险的同时提升患者照护质量。

ABSTRACT

Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.

研究动机与目标

• 通过实现对新冠肺炎患者的远程监测，降低医护人员的感染风险。
• 通过允许轻症患者在家接受监测，缓解医院容量压力。
• 通过物联网与人工智能实现生命体征的持续、实时追踪，支持及时临床决策。
• 确保无需面对面就诊的连续医疗照护，将医疗资源保留给危重病例。

提出的方法

• 部署可穿戴传感器，每秒持续收集生命体征数据——包括体温、呼吸频率、心率、血压和时间。
• 通过物联网将实时生理数据传输至集中监控平台。
• 应用人工智能算法分析数据流，检测异常健康模式。
• 实施双层监测模式：重症患者在医院监测，轻症至中症患者在家监测。
• 设计支持远程诊断与医护人员警报的系统架构。
• 集成决策支持机制，协助医生在无须接触的情况下管理患者状况。

实验结果

研究问题

• RQ1 如何通过远程监测新冠肺炎患者来降低医护人员的感染风险？
• RQ2 何种基于物联网与人工智能的架构能够实现对居家隔离患者生命体征的持续、实时追踪？
• RQ3 远程监测系统如何在疫情期间保护医院对危重病例的收治能力？
• RQ4 实时数据分析在提升非住院患者临床决策质量方面发挥何种作用？
• RQ5 如何通过可扩展系统确保无需面对面就诊的可靠医疗照护？

主要发现

• 该系统能够每秒持续、实时监测体温、呼吸频率、心率和血压等生命体征。
• 远程监测减少了对医院面对面就诊的需求，从而降低了医护人员的暴露风险。
• 通过人工智能分析检测异常生理趋势，该系统支持临床决策制定。
• 它使轻症患者能够安全地在家中接受管理，从而为危重病例保留医院床位。
• 物联网与人工智能的整合提升了疫情期间远程患者照护的可扩展性与响应速度。
• 所提出的框架通过及时干预确保了不间断的医疗照护，并改善了患者预后。