Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM.
Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM.
复制标题
使用对齐引导状态分割 HMM 进行医疗工作流程建模。
DOI：
10.1109/ichi.2017.66
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Burd,RandallS
中科院分区：
文献类型：
--
作者：
Yang,Sen;Zhou,Moliang;Chen,Shuhong;Dong,Xin;Marsic,Ivan;Ahmed,Omar;Burd,RandallS
流程挖掘技术已被用于发现和分析各个领域的工作流，从业务管理到医疗保健。然而，大部分的研究，忽略了隐藏马尔可夫模型（HALGOR）的工作流发现的潜力。我们提出了一种新的发现工作流模型的基础上，观察到的过程执行的痕迹，指导的状态分裂HMM推理算法（AGSS）。我们使用四个真实世界的医疗工作流数据集和其中一个更详细的案例研究，将AGSS与现有方法进行了比较。我们的数值结果表明，AGSS不仅产生更准确的工作流模型，而且更好地代表了底层的过程。此外，通过跟踪对齐来指导状态分裂，AGSS比以前的HMM推理算法效率高得多（O（n））。我们的案例研究结果表明，我们的方法产生了一个更可读和准确的工作流模型，现有的算法。将发现的模型与同一过程的手工专家模型进行比较，我们发现了三个差异。医学专家重新考虑了这三个差异，并将其用于增强专家模型。
Process mining techniques have been used to discover and analyze workflows in various fields, ranging from business management to healthcare. Much of this research, however, has overlooked the potential of hidden Markov models (HMMs) for workflow discovery. We present a novel alignment-guided state-splitting HMM inference algorithm (AGSS) for discovering workflow models based on observed traces of process executions. We compared the AGSS to existing methods using four real-world medical workflow datasets and a more detailed case study on one of them. Our numerical results show that AGSS not only generates more accurate workflow models, but also better represents the underlying process. In addition, with trace alignment to guide state splitting, AGSS is significantly more efficient (by a factor of O(n)) than previous HMM inference algorithms. Our case study results show that our approach produces a more readable and accurate workflow model that existing algorithms. Comparing the discovered model to the hand-made expert model of the same process, we found three discrepancies. These three discrepancies were reconsidered by medical experts and used for enhancing the expert model.