基于对慢性阻塞性肺病患者睡眠质量的调查评估的机器学习模型
Miraç Öz1, Banu Eriş Gülbay1, Barış Bulut1
1Department of Chest Diseases, Faculty of Medicine, Ankara University, Ankara, Türkiye.
PloS one
|May 21, 2025
概括
机器学习准确地预测慢性阻塞性肺病 (COPD) 患者的睡眠质量. 诸如恶化,白天嗜睡和吸烟等因素会对COPD睡眠质量产生负面影响,而治疗冠状动脉疾病可能会改善睡眠质量.
科学领域:
- 肺部医学 肺部医学
- 睡眠科学 睡眠科学
- 医疗保健中的机器学习
背景情况:
- 在患有慢性阻塞性肺病 (COPD) 的患者中,睡眠障碍很常见.
- 睡眠质量不佳显著影响COPD患者的健康和生活质量.
- 识别影响COPD睡眠的因素对于有针对性的干预至关重要.
研究的目的:
- 通过使用临床和调查数据,开发COPD患者睡眠质量的预测模型.
- 确定与COPD睡眠质量差相关的关键临床和人口因素.
- 利用机器学习来增强对呼吸系统疾病中的睡眠的理解.
主要方法:
- 利用匹兹堡睡眠质量指数 (PSQI) 来评估稳定的COPD患者的睡眠质量.
- 使用递归特征消除 (RFE) 和贝叶斯信息标准 (BIC) 来从61个变量中选择预测参数.
- 开发了一个后勤回归 (LR) 模型,使用嵌套交叉验证与1000个引导代进行验证.
主要成果:
- 该研究包括132名COPD患者 (88.6%是男性,平均年龄为66.68岁);68人睡眠质量差.
- 睡眠质量差与呼吸障碍,打,目击性呼吸暂停和过度白天嗜睡 (EDS) 的更高率有关.
- 睡眠不足的关键预测因素包括高年恶化/住院率,EDS,咳,活跃吸烟,SABA使用和高pH水平. 该模型实现了70.99%的准确性和0.757 AUC.
结论:
- 一个机器学习模型有效地预测了COPD患者的睡眠质量.
- 负面影响COPD睡眠质量的因素包括频繁的恶化,EDS,咳,活跃吸烟,SABA使用和高pH.
- 经过治疗的冠状动脉疾病 (CAD) 被发现对这一COPD队列的睡眠质量产生了积极的影响.
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