A Proactive Workflow Model for Healthcare Operation and Management
A Proactive Workflow Model for Healthcare Operation and Management
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DOI：
10.1109/tkde.2016.2631537
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发表时间：
2017-03-01
影响因子：
8.9
通讯作者：
Xiao, Keli
中科院分区：
文献类型：
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作者：
Liu, Chuanren;Xiong, Hui;Xiao, Keli
实时定位系统的进步使我们能够收集大量的细粒度语义丰富的位置轨迹，这为理解人类活动和生成有用的知识提供了无与伦比的机会。这反过来又为各个领域（如工作流管理）的实时决策提供了智能。事实上，这是一个新的范例，通过知识发现的位置跟踪工作流模型。为此，在本文中，我们提供了一个集中的研究工作流建模的综合分析，在医院环境中的室内位置痕迹。特别是，我们开发了一个工作流建模框架，自动构建的工作流状态和估计参数描述的工作流转换模式。更具体地说，我们提出了有效和高效的正则化建模的室内位置跟踪随机过程。首先，为了提高工作流状态的可解释性，我们使用室内房间之间的地理关系来定义工作流状态分布的先验。这种先验鼓励每个工作流状态成为建筑物中的连续区域。其次，为了进一步提高建模性能，我们展示了如何使用相关类型的医疗设备之间的相关性，以加强多个工作流模型的参数估计。与我们的前期工作[11]相比，我们不仅开发了一个适用于一般室内环境的集成工作流建模框架，而且显著提高了建模精度。我们将平均日志损失降低了11%。
Advances in real-time location systems have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and generating useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm to model workflows through knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by integrated analysis of indoor location traces in the hospital environment. In particular, we develop a workflow modeling framework that automatically constructs the workflow states and estimates the parameters describing the workflow transition patterns. More specifically, we propose effective and efficient regularizations for modeling the indoor location traces as stochastic processes. First, to improve the interpretability of the workflow states, we use the geography relationship between the indoor rooms to define a prior of the workflow state distribution. This prior encourages each workflow state to be a contiguous region in the building. Second, to further improve the modeling performance, we show how to use the correlation between related types of medical devices to reinforce the parameter estimation for multiple workflow models. In comparison with our preliminary work [11], we not only develop an integrated workflow modeling framework applicable to general indoor environments, but also improve the modeling accuracy significantly. We reduce the average log-loss by up to 11 percent.