Analyzing the Trajectories of Patients with Sepsis using Process Mining
Analyzing the Trajectories of Patients with Sepsis using Process Mining
复制标题
使用过程挖掘分析脓毒症患者的轨迹
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Daan Blinde
中科院分区：
文献类型：
--
作者：
F. Mannhardt;Daan Blinde
过程挖掘技术基于事件数据分析过程。我们分析了荷兰一家医院的病人从他们在急诊室登记到出院的轨迹。我们考虑了1050例有脓毒症症状的患者，这是一种危及生命的疾病。我们提取了一个事件日志，其中包括急诊室活动、住院和出院的事件。事件日志中添加了来自实验室测试和分类检查表的数据。我们试图自动发现病人轨迹的过程模型，我们检查是否符合败血症患者的医疗指南，并在一个非法律过程模型上可视化病人的流动。从该分析中得到的经验教训是：(1)流程挖掘可用于澄清医院的患者流程；(2)流程挖掘可用于对照医疗指南检查日常临床实践；(3)过程发现方法可能会返回不合适的模型，使利益相关者难以理解；(4)过程挖掘是一个迭代过程，例如，数据质量问题经常被发现并需要解决。
Process mining techniques analyze processes based on event data. We analyzed the trajectories of patients in a Dutch hospital from their registration in the emergency room until their discharge. We considered a sample of 1050 patients with symptoms of a sepsis condition, which is a life-threatening condition. We extracted an event log that includes events on activities in the emergency room, admission to hospital wards, and discharge. The event log was enriched with data from laboratory tests and triage checklists. We try to automatically discover a process model of the patient trajectories, we check conformance to medical guidelines for sepsis patients, and visualize the flow of patients on a de-jure process model. The lessons-learned from this analysis are: (1) process mining can be used to clarify the patient flow in a hospital; (2) process mining can be used to check the daily clinical practice against medical guidelines; (3) process discovery methods may return unsuitable models that are difficult to understand for stakeholders; and (4) process mining is an iterative process, e.g., data quality issues are often discovered and need to be addressed.
DOI：
10.1197/jamia.m1135
发表时间：
2003-01-01
影响因子：
6.4
作者：
Peleg, M;Tu, S;Stefanelli, M
通讯作者：
Stefanelli, M