Towards an Intelligent Monitoring System for Patients with Obstrusive Sleep Apnea

Abstract

Due to the growing incidence of chronic diseases and aging populations, the pressure to control costs and the expectations of continuous improvements in the quality of service have increased the need to understand how healthcare is provided and to determine whether cost-effective improvements to care practices can be made. In the case of people suffering Obstructive Sleep Apnea, patients using self-administer nasal Continuous Positive Airway Pressure (CPAP) may receive information on the treatment only once they go to a visit with the lung specialist. In this paper, we propose an IoT-based Intelligent Monitoring System that relies on machine learning to achieve a threefold goal: (1) it is aimed at early detecting compliance in order to predict CPAP usage; (2) it monitors the actual adherence degree to the treatment to keep informed both the patient and the lung specialists; and (3) it sends recommendations to the patient to empower her/him and to better follow up.

Comparative analysis of predictive methods for early assessment of compliance with continuous positive airway pressure therapy

“…Finally, let us point out that the work proposed in this paper is part of the myOSA project (RTC-2014-3138-1), aimed at developing new ICT tools to support the OSA treatment. Under the umbrella of myOSA, we created an IoT system [ 57 ] that remotely monitors the patient’s CPAP devices to provide indicators of progress such as early compliance, adherence level, as well as personalized recommendations to empower the patients. Moreover, we are currently investigating how to apply these predictive methods also in the context of the H2020 project CONNECARE (ID: 689802), in which we focus on patient’s monitoring with the final goal of providing self-management features to people in needs, such as chronic patients [ 58 ].…”

Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial

“…Hospital lung specialists managing these patients and the CPAP provider (Oxigen salud) also had access to the MiSAOS website that provided relevant information and decision support according to the specific role and access rights of each professional user. Finally, the cloud-based MiSAOS platform connected all the devices for data exchange and hosted an intelligent monitoring system, based on machine learning, capable of predicting the expected compliance with the therapy by a given patient, thus providing adequate feedback and proposing personalized interventions to increase compliance [ 15 , 18 ]. Predictions of patient’s midterm compliance were based on patient’s characteristics, such as anthropometric data and clinical information, and early compliance data.…”

“…By contrast, when these strategies are wrapped up in comprehensive packages making use of information and communication technologies (eHealth) and targeting the initial months after CPAP prescription, the potential for success can be significantly enhanced [ 12 - 14 ]. In this scenario, and within the frame of the MiSAOS project, an internet of things–based intelligent monitoring system relying on machine learning [ 15 ] was developed in Catalonia, Spain, with a fourfold goal: (1) predicting patient’s potential early CPAP compliance; (2) providing real-time monitoring of patient’s CPAP compliance, informing both the patient and the care team, and granting decision support; (3) empowering the patient by means of feedback and recommendations; and (4) reducing patient’s overall management costs. This study compares, in terms of effectiveness and cost-effectiveness, the MiSAOS intelligent monitoring system model, based on early compliance detection, compliance prediction, and rule-based recommendations, with the usual care provided to patients using CPAP in the region of Lleida, Catalonia.…”

Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial (Preprint)

“…By contrast, when these strategies are wrapped up in comprehensive packages making use of information and communication technologies (eHealth) and targeting the initial months after CPAP prescription, the potential for success can be significantly enhanced [12][13][14]. In this scenario, and within the frame of the MiSAOS project, an internet of things-based intelligent monitoring system relying on machine learning [15] was developed in Catalonia, Spain, with a fourfold goal: (1) predicting patient's potential early CPAP compliance; (2) providing real-time monitoring of patient's CPAP compliance, informing both the patient and the care team, and granting decision support; (3) empowering the patient by means of feedback and recommendations; and (4) reducing patient's overall management costs. This study compares, in terms of effectiveness and cost-effectiveness, the MiSAOS intelligent monitoring system model, based on early compliance detection, compliance prediction, and rule-based recommendations, with the usual care provided to patients using CPAP in the region of Lleida, Catalonia.…”