使用机器学习模型来识别在更年期过渡期间护士的严重主观认知衰退和相关因素:一个试点研究
Xiangyu Zhao1, Xiaona Shen1, Fengcai Jia2
1From the School of Nursing and Rehabilitation, Shandong University, Jinan, Shandong, China.
概括
机器学习模型有效地识别了在更年期期间护士的严重主观认知衰退 (SCD). 更年期症状和阶段是关键因素,为认知健康管理提供了洞察力.
科学领域:
- 认知神经科学 认知神经科学
- 妇女健康 妇女健康
- 医疗保健中的人工智能
背景情况:
- 主观认知衰退 (SCD) 在更年期过渡期间越来越令人担忧.
- 识别有风险的个人和相关因素对于及时干预至关重要.
研究的目的:
- 开发和验证机器学习模型,以识别更年期期间护士的严重SCD.
- 为了确定与该人群中严重的SCD相关的因素.
主要方法:
- 来自1,264名护士的横截面数据的二次分析.
- 利用Bortua算法进行特征选择,并开发了七个机器学习模型.
- 使用AUC,精度,灵敏度,特异性和F1评分来评估模型性能;采用了沙普利的附加解释.
主要成果:
- 平均SCD得分为5.38±2.43. 平均SCD得分是5.38±2.43. 平均SCD得分是5.38±2.43. 平均SCD得分是5.38±2.43. 平均SCD得分是5.38±2.43.
- 一种支向量机器模型显示出最佳性能 (AUC:0.846).
- 更年期症状和更年期的阶段是严重SCD最强的预测因素.
结论:
- 机器学习模型可以有效地识别更年期护士的严重SCD和相关因素.
- 研究结果为在更年期过渡期间管理认知健康提供了宝贵的见解.
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