A1 Journal article – refereed
Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health




List of Authors: Azimi I., Pahikkala T., Rahmani A., Niela-Vilén H., Axelin A., Liljeberg P.
Publisher: Elsevier B.V.
Publication year: 2019
Journal: Future Generation Computer Systems
Journal name in source: Future Generation Computer Systems
Volume number: 96
ISSN: 0167-739X
eISSN: 1872-7115

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.


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Last updated on 2019-16-04 at 13:32