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开发一种机器学习算法,以利用人与智能手机交互模式预测医务人员工作模式的概率:算法开发和验证研究

Hung-Hsun Chen1,2, Henry Horng-Shing Lu3,4, Wei-Hung Weng5

  • 1Department of Mathematics, Fu Jen Catholic University, New Taipei City, Taiwan.

Journal of medical Internet research
|December 29, 2023
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概括
此摘要是机器生成的。

这项研究引入了一种新的机器学习方法",工作模式中的概率",通过分析智能手机交互和GPS数据来准确估计工作时间. 与传统的GPS跟踪相比,这种新的方法显示了更长的工作时间,包括大量的远程工作时间.

关键词:
深度学习是一种深度学习.数字化表型化是指数字化表型化.极端的梯度增强了树木的树木.人与智能手机的互动机器学习是机器学习.一维卷积神经网络的一个维度.在工作模式中的概率.工作时间 工作时间

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科学领域:

  • 人与计算机的交互
  • 机器学习应用 机器学习应用
  • 职业健康 职业健康 职业健康

背景情况:

  • 传统的工作时间跟踪方法受到实体存在的限制,难以准确测量远程工作和休息时间.
  • 在工作时间估计方面,区分现场休息和现场外远程工作是一个重大挑战.
  • 机器学习提供了在工作期间与非工作期间区分人与智能手机交互的潜力.

研究的目的:

  • 开发和验证一种新的方法,称为"工作模式中的概率",用于精确的工作小时估计.
  • 利用人与智能手机的交互模式和GPS数据来更准确地评估工作时间.
  • 使用机器学习模型区分办公室工作,远程工作和休息时间.

主要方法:

  • "员工时间"应用程序被动记录参与者的屏幕事件 (时间,通知,应用程序使用) 和GPS位置.
  • 使用极端梯度增强树和1维卷积神经网络来处理交互模式并生成概率.
  • "工作模式中的概率"输出区分办公室工作,工作外,现场休息和远程工作.

主要成果:

  • 该研究包括121名参与者超过5503人-天,实现模型预测性能 (AUC) 0.915.
  • 根据"工作模式的概率" (11.2小时/天) 估计的工作时间远远长于GPS定义的时间 (10.2小时/天).
  • 这一差异主要是由于远程工作时间 (111.6分钟) 与休息时间 (54.7分钟) 相比估计更高.

结论:

  • "工作模式中的概率"方法通过整合人与智能手机的交互和机器学习来提高工作时间调查的准确性.
  • 这种方法为复杂的工作模式提供了宝贵的见解,包括远程工作和休息时间.
  • 这些发现表明,对于优化工作生产力和员工幸福感的潜在应用.