부산대학교 도서관

Obstructive Sleep Apnea (OSA) is a potentially severe sleep disorder that leads to different pathology. The goal treatment to OSA is the Positive Airway Pressure (PAP) therapy. Nevertheless, this therapy has one of the lowest compliance levels when compared to the other 17 therapies. For the last two decades, trials were carried out to improve this compliance level and understand factors impacting compliance, but there were no conclusive results. In this paper, we propose a framework for modeling multiple patient profiles at a different moment in the PAP therapy. This approach in PAP therapy takes into consideration multiple factors and the interactions between the factors at a specific moment in the therapy to understand and tackle the compliance problem. The data pre-processing is implemented in Python to extract the factors from the raw data. The processing and the core features of the framework are implemented in R. Six different patient profile was identified based on the event recorded between 3 days and 15 days after the installation of the PAP device at the patient home.