阻塞性睡眠呼吸暂停
健康信息学
操作化
医学
睡眠呼吸暂停
计算机科学
重症监护医学
信息学
睡眠(系统调用)
公共卫生
护理部
内科学
工程类
哲学
电气工程
操作系统
认识论
作者
Maria Rizoneide Negreiros de Araújo,Louis Kazaglis,Conrad Iber,Jaideep Srivastava
标识
DOI:10.1109/bigdata47090.2019.9006476
摘要
The abandonment rate of patients who use CPAP devices for obstructive sleep apnea (OSA) therapy is as high as 60%. However, there is growing evidence that timely and appropriate intervention can improve long-term adherence to therapy. Current practice in sleep clinics of identifying potential patients who will abandon the treatment is not sufficiently effective in terms of accuracy and timeliness. Recent proposals in the literature have tried to identify non-adherent patients in a specific period of their therapy; however, there is no generalized approach by which clinical providers can monitor their patients continually with the goal of maximizing adherence. Towards this more generic goal, we propose CTAP-CPAP, a Continuous Treatment Adherence Prediction framework. With CTAP-CPAP, we address the problem of generalizing the prediction for any day in the treatment, where a robust framework with multiple machine learning models is implemented to assist medical practitioners keep track of the patient risk of non-adherence. Aiming the parallel progress of both machine learning and health informatics fields, we complement the study with a transparent discussion on the machine learning techniques used to build CTAP-CPAP and our view of its operationalization in a sleep clinic.
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