مشخصات مقاله:
عنوان فارسی مقاله:
اتخاذ تصمیم انعطاف پذیر داده مفقود برای اینترنت اشیای بهداشت و درمان از طریق شخصی سازی: مطالعه موردی در مورد سلامت مادران
عنوان انگلیسی مقاله:
Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health
کلمات کلیدی مقاله:
داده مفقود، نظارت بلند مدت، نظارت بر سلامت، اینترنت اشیا، مراقبت از مادران، تصمیم گیری شخصی
مناسب برای رشته های دانشگاهی زیر:
مهندسی فناوری اطلاعات – مهندسی کامپیوتر
مناسب برای گرایش های دانشگاهی زیر:
شبکه های کامپیوتری – اینترنت و شبکه های گسترده – الگوریتم و محاسبات
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Outline
Highlights
Abstract
Keywords
۱٫ Introduction
۲٫ Background and related work
۳٫ Missing data resilient decision-making approach
۴٫ Demonstration and evaluation
۵٫ Conclusion
Acknowledgments
References
قسمتی از مقاله انگلیسی:
Abstract
Remote health monitoring is an effective method to enable tracking of at-risk patients outside of conventional clinical settings, providing early-detection of diseases and preventive care as well as diminishing healthcare costs. Internet-of-Things (IoT) technology facilitates developments of such monitoring systems although significant challenges need to be addressed in the real-world trials. Missing data is a prevalent issue in these systems, as data acquisition may be interrupted from time to time in long-term monitoring scenarios. This issue causes inconsistent and incomplete data and subsequently could lead to failure in decision making. Analysis of missing data has been tackled in several studies. However, these techniques are inadequate for real-time health monitoring as they neglect the variability of the missing data. This issue is significant when the vital signs are being missed since they depend on different factors such as physical activities and surrounding environment. Therefore, a holistic approach to customize missing data in real-time health monitoring systems is required, considering a wide range of parameters while minimizing the bias of estimates. In this paper, we propose a personalized missing data resilient decision-making approach to deliver health decisions 24/7 despite missing values. The approach leverages various data resources in IoT-based systems to impute missing values and provide an acceptable result. We validate our approach via a real human subject trial on maternity health, in which 20 pregnant women were remotely monitored for 7 months. In this setup, a real-time health application is considered, where maternal health status is estimated utilizing maternal heart rate. The accuracy of the proposed approach is evaluated, in comparison to existing methods. The proposed approach results in more accurate estimates especially when the missing window is large.
1. Introduction
Remote health monitoring systems broadly extend the boundaries of everyday healthcare access particularly for at-risk population groups including pregnant women [1] and senior adults [2] who may require additional observation. These systems are very promising in the healthcare domain as the individuals can be continuously monitored for early detection, preventive care, and early intervention. The key function of such healthcare systems is to ubiquitously observe and analyze users’ health conditions, and subsequently deliver medical early-warning as well as health and wellness coaching. Fortunately, recent advances in Internet-of-Things (IoT) technologies have paved the way for enabling such monitoring services with 24/7 availability. IoT is a growing network of interconnected objects that envision a shared knowledge for smart and autonomous decision-making and actuation [3, 4, 5, 6]. In the healthcare domain, IoT systems leverage different sensing, computing and communication resources.