描述睡眠障碍子组和鉴定传统中医护士中的相关因素:潜伏的个人资料分析和可解释的机器学习方法
Chong Liu1, Nieran Lian2, Kristin K Sznajder3
1Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China, cmu.edu.cn.
Journal of nursing management
|February 26, 2026
概括
传统中医 (TCM) 护士经历了显著的睡眠障碍,潜伏特征分析确定了三个不同的子组. 机器学习模型有效地区分了这些群体,突出了针对有针对性的干预措施的收入和弹性等因素.
科学领域:
- 护理 护理 护理
- 睡眠科学 睡眠科学
- 计算健康 计算健康
背景情况:
- 传统中医 (TCM) 护士面临着相当大的职业压力影响睡眠.
- 有限的以个人为中心的研究存在,用于对这一群体的睡眠障碍模式进行分类.
研究的目的:
- 在TCM护士中使用隐性概况分析 (LPA) 识别不同的睡眠障碍子组.
- 评估可解释的机器学习模型在根据人口和职业因素对这些子组进行歧视方面的有效性.
主要方法:
- 对7721名TCM护士进行了横截面调查.
- 潜伏特征分析 (LPA) 分类了睡眠障碍模式.
- 可解释的机器学习模型,包括XGBoost与SHAP分析,用于分组歧视.
主要成果:
- 确定了三个睡眠障碍子组:轻度稳定 (29.8%),中度波动 (60%) 和严重持久 (10.2%).
- XGBoost模型获得了最高的歧视性性能 (AUC = 0.84).
- 分类的关键特征包括月收入,组织支持,医院水平,自我同情和弹性.
结论:
- 该研究成功地描述了TCM护士的三个睡眠障碍概况,其中大多数属于中度类别.
- 可解释的机器学习有效地区分了这些子组.
- 识别收入和性等相关因素可以为面临严重睡眠障碍风险的护士提供有针对性的干预措施.
相关概念视频
01:24Substance Use Disorders Affecting Sleep
484
Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
Understanding the concepts of physical dependence,...
484
01:19Management of Insomnia
740
The sleep cycle, an integral part of human health, consists of several stages with distinct characteristics and functions. It begins with a transition from wakefulness to sleep, known as the light sleep phase, followed by the restorative deep sleep phase, essential for physical recovery and growth. The cycle concludes with the Rapid Eye Movement (REM) phase, characterized by high brain activity and vivid dreaming. Insomnia, a prevalent sleep disorder, involves difficulty falling asleep, staying...
740