Facilitating CPAP adherence with personalized recommendations using artificial neural networks

Sleep apnea is a common sleep disorder that, if left untreated, can have critical complications to the individual. The most common and effective treatment for sleep apnea is the Continuous Positive Airway Pressure (CPAP) therapy. But it has a long-term adherence rate as low as 60% due to discomfort and other factors. Although previous research has attempted to increase CPAP usage, there has been little to no change in its average adherence for the past two decades. This paper attempts to change this scenario using a large longitudinal dataset combined with a Recurrent Neural Network model to generate therapy use recommendations after one month of therapy. We performed a retrospective cohort analysis on 3380 patients during their first six months of therapy and compared our personalized recommendation system with the current generic recommendations made by sleep physicians. We show that recommendations generated by our artificial neural network model are easier to achieve since they are significantly closer to patients' therapy progress while being equally successful in maintaining therapy adherence.