Alharbi Amirah, Bulpitt Andy, Johnson Owen A
School of Computing University of Leeds, Leeds, UK.
Stud Health Technol Inform. 2018;247:381-385.
Process mining techniques can play a significant role in understanding healthcare processes by supporting analysis of patient records in electronic health record systems. Healthcare processes are complex and patterns of care may vary considerably within similar cohorts of patients. Process mining often creates "spaghetti" models and require significant domain expert input to refine. Machine learning approaches such as Hidden Markov Models (HMM) may assist this refinement process. HMMs have been advocated for patient pathways clustering purposes; however these models can also be utilized for detecting hidden processes to help event abstraction. We explore use of an unsupervised method for detecting hidden healthcare sub-processes using HMMs, in particular the Viterbi algorithm. We describe an approach to enrich the event log with HMM-derived states and remodeling the healthcare processes as state transitions using a process mining tool. Our method is applied to event data for 'Altered Mental Status' patients that was extracted from a US hospital database (MIMIC-III). The results are promising and show a successful reduction of model complexity and detection of several hidden processes unsupervised by a domain expert.
过程挖掘技术通过支持对电子健康记录系统中的患者记录进行分析，在理解医疗保健过程中可以发挥重要作用。医疗保健过程很复杂，在相似的患者群体中护理模式可能有很大差异。过程挖掘通常会创建“ spaghetti”模型，并且需要大量领域专家的投入来进行优化。诸如隐马尔可夫模型（HMM）之类的机器学习方法可能有助于此优化过程。HMM已被提倡用于患者路径聚类目的；但是，这些模型也可用于检测隐藏过程以帮助进行事件抽象。我们探索使用一种无监督方法，即使用HMM（特别是维特比算法）来检测隐藏的医疗子过程。我们描述了一种方法，用HMM派生的状态丰富事件日志，并使用过程挖掘工具将医疗保健过程重塑为状态转换。我们的方法应用于从美国医院数据库（MIMIC-III）中提取的“精神状态改变”患者的事件数据。结果很有希望，显示出成功降低了模型复杂性，并检测到了一些未经领域专家监督的隐藏过程。
