万达:针对心力衰竭患者的端到端远程健康监测和分析系统

M. Lan, Lauren Samy, N. Alshurafa, Myung-kyung Suh, Hassan Ghasemzadeh, Aurelia Macabasco-O'Connell, M. Sarrafzadeh
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引用次数: 65

摘要

无线传感器、移动技术和云计算的最新进展使持续远程监测患者成为可能。本文介绍了为心力衰竭患者设计的端到端远程健康监测与分析系统WANDA的设计与实现。该系统包括一个基于智能手机的数据收集网关、一个互联网规模的数据存储和搜索系统,以及一个用于诊断和预测目的的后端分析引擎。该系统支持从各种测量患者生命体征的传感设备以及自我报告的问卷中收集数据。分析引擎的主要目标是通过检查患者的生理读数来预测未来的事件。我们使用从18名心力衰竭患者的试点研究中收集的数据来证明所提出的分析引擎的效率。特别是,我们的研究结果表明,我们系统中使用的先进分析算法能够预测患者心力衰竭症状的恶化,准确率高达74%,同时与常用的基于每日体重变化的阈值算法相比,灵敏度性能提高了45%以上。此外,我们的系统所获得的精度仅比理论上限低9%。提出的框架目前在一项针对1500名充血性心力衰竭患者的大型正在进行的心力衰竭研究中部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WANDA: an end-to-end remote health monitoring and analytics system for heart failure patients
Recent advances in wireless sensors, mobile technologies, and cloud computing have made continuous remote monitoring of patients possible. In this paper, we introduce the design and implementation of WANDA, an end-to-end remote health monitoring and analytics system designed for heart failure patients. The system consists of a smartphone-based data collection gateway, an Internet-scale data storage and search system, and a backend analytics engine for diagnostic and prognostic purposes. The system supports the collection of data from a wide range of sensory devices that measure patients' vital signs as well as self-reported questionnaires. The main objective of the analytics engine is to predict future events by examining physiological readings of the patients. We demonstrate the efficiency of the proposed analytics engine using the data gathered from a pilot study of 18 heart failure patients. In particular, our results show that the advanced analytic algorithms used in our system are capable of predicting the worsening of patients' heart failure symptoms with up to 74% accuracy while improving the sensitivity performance by more than 45% compared to the commonly used thresholding algorithm based on daily weight change. Moreover, the accuracy attained by our system is only 9% lower than the theoretical upper bound. The proposed framework is currently deployed in a large ongoing heart failure study that targets 1500 congestive heart failure patients.
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