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A Decision Support System for Tele-Monitoring COPD-Related Worrisome Events | IEEE Journals & Magazine | IEEE Xplore

A Decision Support System for Tele-Monitoring COPD-Related Worrisome Events


Abstract:

Chronic Obstructive Pulmonary Disease (COPD) is a preventable, treatable, and slowly progressive disease, whose course is aggravated by a periodic worsening of symptoms a...Show More

Abstract:

Chronic Obstructive Pulmonary Disease (COPD) is a preventable, treatable, and slowly progressive disease, whose course is aggravated by a periodic worsening of symptoms and lung function lasting for several days. The development of home telemonitoring systems has made possible to collect symptoms and physiological data in electronic records, boosting the development of decision support systems (DSSs). Current DSSs work with physiological measurements collected by means of several measuring and communication devices as well as with symptoms gathered by questionnaires submitted to COPD subjects. However, this contrasts with the advices provided by the World Health Organization and the Global initiative for chronic Obstructive Lung Disease that recommend to avoid invasive or complex daily measurements. For these reasons this manuscript presents a DSS detecting the onset of worrisome events in COPD subjects. It uses the hearth rate and the oxygen saturation, which can be collected via a pulse oximeter. The DSS consists in a binary finite state machine, whose training stage allows a subject specific personalization of the predictive model, triggering warnings, and alarms as the health status evolves over time. The experiments on data collected from 22 COPD patients tele-monitored at home for six months show that the system recognition performance is better than the one achieved by medical experts. Furthermore, the support offered by the system in the decision-making process allows to increase the agreement between the specialists, largely impacting the recognition of the worrisome events.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21, Issue: 2, March 2017)
Page(s): 296 - 302
Date of Publication: 17 January 2017

ISSN Information:

PubMed ID: 28103562

I. Introduction

Chronic Obstructive Pulmonary Disease (COPD) is a common preventable and treatable disease characterized by persistent airflow limitations, slowly progressive, partially or totally irreversible, and associated with an enhanced chronic inflammatory response to noxious particles or gases in the airways and in the lung [1]. As the disease progresses, the patients lose efficiency of respiratory muscles and the airways undergo progressive obstruction. COPD is currently a major cause of chronic morbidity and mortality throughout the world and it is predicted to become the main one by 2020 [2]. Patients affected by COPD also suffer from periodic worsening of symptoms and lung function lasting for several days, such as exacerbations, tachycardia, dyspnoea and hypoxemia [3]. Although early intervention with antibiotics and steroids can prevent COPD-related hospital admissions, several patients are not able to promptly recognize early signs and symptoms of these worrisome events from day-to-day variations. These events induce deteriorations in respiratory health and aggravate the course of the disease, resulting in larger use of healthcare services and lower health-related quality of life (HRQoL) [4].

References

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