Data-oriented approach to improve adherence to CPAP therapy during the initiation phase

Abstract

Obstructive Sleep Apnea (OSA) is a sleep pathology that leads to different illness. The goal therapy for OSA is a Continuous Positive Airway Pressure (CPAP). However, CPAP therapy is one of the therapies which has the lowest adherence level. This paper presents a data-driven framework to improve the experience of the patients during the initiation phase of CPAP therapy. Since this phase is a key factor for adherence level over time. Our approach uses data analytics techniques to provide personalised services for each patient through a different process of knowledge discovery. We have integrated a validation process for each outcome of the framework, Therefore, there is also continuous improvement of the data models.