Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health
Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health
复制标题
DOI：
10.1016/j.future.2019.02.015
复制
发表时间：
2019-07-01
影响因子：
7.5
通讯作者：
Liljeberg, Pasi
中科院分区：
文献类型：
--
作者：
Azimi, Iman;Pahikkala, Tapio;Liljeberg, Pasi
远程健康监测是一种有效的方法，可以在传统临床环境之外跟踪高危患者，提供疾病的早期检测和预防性护理，并降低医疗成本。物联网（IoT）技术促进了这种监测系统的发展，尽管在现实世界的试验中需要解决重大挑战。数据缺失是这些系统中普遍存在的问题，因为在长期监测情况下，数据采集可能会不时中断。这个问题会导致不一致和不完整的数据，随后可能导致决策失败。缺失数据的分析已在几项研究中得到解决。然而，这些技术不足以进行实时健康监测，因为它们忽略了缺失数据的可变性。当生命体征被遗漏时，这个问题是重要的，因为它们取决于不同的因素，例如身体活动和周围环境。因此，需要一种全面的方法来定制实时健康监测系统中的缺失数据，考虑各种参数，同时最大限度地减少估计值的偏差。在本文中，我们提出了一种个性化的缺失数据弹性决策方法，以提供24/7的健康决策，尽管缺失值。该方法利用基于物联网的系统中的各种数据资源来估算缺失值并提供可接受的结果。我们通过一个关于产妇健康的真实的人类受试者试验来验证我们的方法，在该试验中，20名孕妇接受了7个月的远程监测。在此设置中，考虑实时健康应用，其中利用产妇心率估计产妇健康状况。所提出的方法的准确性进行评估，与现有的方法相比。所提出的方法的结果更准确的估计，特别是当丢失的窗口是大的。(C)2019作者由爱思唯尔公司出版
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. (C) 2019 The Authors. Published by Elsevier B.V.