临床护士睡眠障碍风险预测:一个名图和人工神经网络模型
1The First Affiliated Hospital of Jinzhou Medical University, 121001, Jinzhou, People's Republic of China.
BMC nursing
|August 28, 2023
概括
临床护士经常经历睡眠障碍. 这项研究开发了预测模型,Nomogram和人工神经网络 (ANN),以识别有睡眠障碍风险的护士,帮助早期干预.
科学领域:
- 护理研究 护理研究
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
背景情况:
- 睡眠障碍在护士中非常普遍.
- 这种高发病率需要有效的预测工具,以便及时干预.
研究的目的:
- 开发和验证临床护士睡眠障碍的预测模型.
- 该研究旨在创建一个Nomogram和一个人工神经网络 (ANN) 模型.
主要方法:
- 一项涉及434名临床护士的横截面研究.
- 从2021年8月到2022年6月收集的数据.
- 使用已识别的风险因素构建了Nomogram和ANN模型,并通过引导重新抽样和ROC分析进行了验证.
主要成果:
- 睡眠障碍在培训队列中影响了59.2%,在验证队列中影响了61.5%.
- 关键预测因素包括年龄,慢性疾病,焦虑,抑郁,倦怠和疲劳.
- 两种模型都表现出良好的校准,在预测睡眠障碍方面具有高灵敏度和特异性.
结论:
- 开发的Nomogram和ANN模型有效地预测临床护士的睡眠障碍.
- 这些模型显示出良好的一致性和预测效率.
- 它们为识别有风险的护士和促进预防策略提供了有价值的工具.
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