基于机器学习的个性化预测健康和同理心在医疗保健专业人员
Jason Nan1,2, Matthew S Herbert3,4,5, Suzanna Purpura1,3
1Neural Engineering and Translation Labs, University of California San Diego, La Jolla, CA 92093, USA.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
个性化的机器学习模型可以预测医疗保健专业人员的福祉和同情心. 个人因素,如情绪和社会联系,是关键因素,表明定制干预措施比一刀切的方法更有效.
科学领域:
- 医疗保健专业人员的福祉
- 医学中的同理心在医学中.
- 个性化机器学习是个性化的机器学习.
背景情况:
- 医疗保健专业人员经历了相当大的工作场所压力和倦怠.
- 关于幸福感和同情心的群体研究忽视了个体差异.
- 需要个性化干预来提高护理质量.
研究的目的:
- 实施N-of-1个性化机器学习 (PML) 来预测个人福祉和同情心.
- 确定影响医疗保健专业人员福祉和同情心的关键个人因素.
- 评估个性化干预策略的潜力.
主要方法:
- 使用了生态瞬间评估 (EMA) 和智能手表数据.
- 收集了47个日常情绪和生活方式变量,长达三个月.
- 在PML管道中应用了八个监督机器学习 (ML) 模型.
主要成果:
- 个性化的模型揭示了参与者之间不同的顶级预测因素.
- 焦虑和抑郁的情绪是最常见的预测因素.
- 社会联系对幸福很重要,但不是同理心;幸福和同理心相互预测.
结论:
- 共同和个人因素影响医疗保健专业人员的福祉和同情心.
- 一种适合所有人的方法来改善福祉和同情心是不理想的.
- PML模型为个性化干预提供了可操作的见解,以提高健康和患者护理.
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