Facilitating CPAP Adherence with Personalized Recommendations Using Artificial Neural Networks
Facilitating CPAP Adherence with Personalized Recommendations Using Artificial Neural Networks
复制标题
使用人工神经网络通过个性化建议促进 CPAP 坚持
DOI：
10.1109/cbms52027.2021.00093
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Iber, Conrad
中科院分区：
文献类型：
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作者：
Araujo, Matheus;Pereira, Tara;Srivastava, Jaideep;Iber, Conrad
睡眠呼吸暂停是一种常见的睡眠障碍，如果不及时治疗，可能会对个体产生严重的并发症。睡眠呼吸暂停最常见和最有效的治疗方法是持续气道正压通气（CPAP）治疗。但由于不适和其他因素，它的长期坚持率低至60%。虽然以前的研究试图增加CPAP的使用，但在过去的二十年中，其平均依从性几乎没有变化。本文试图使用大型纵向数据集结合递归神经网络模型来改变这种情况，以在治疗一个月后生成治疗使用建议。我们对3380名患者在治疗的前六个月进行了回顾性队列分析，并将我们的个性化推荐系统与睡眠医生目前的一般建议进行了比较。我们表明，由我们的人工神经网络模型生成的建议更容易实现，因为它们更接近患者的治疗进展，同时在维持治疗依从性方面同样成功。
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.