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Context-Aware change pattern detection in Event Attributes of Recurring Activities 1

35th CAiSE Conference on Cyber-Human Systems

作者： Cremerius, Jonas Weske, Mathias Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (数字)9783031346743

ISBN: (纸本)9783031346736;9783031346743

Process mining bridges the gap between process management and data science by utilizing process execution data to discover and analyse business processes. This data is represented in event logs, where each event contains attributes describing the process instance, the time the event has occurred, and much more. In addition to these generic event attributes, events contain domain-specific event attributes, such as a measurement of blood pressure in a healthcare environment. Taking a close look at those attributes, it turns out that the respective values change during a typical process quite frequently, hence we refer to them as dynamic event attributes. This paper studies change patterns of dynamic event attributes by recurring process activities in a given context. We have applied the technique on two real-world datasets, MIMIC-IV and Sepsis, representing hospital treatment processes, and show that the approach can provide novel insights. The approach is implemented in Python, based on the PM4Py framework.

关键词： Process Mining change pattern detection Recurring Activities Context-Aware

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Relationships Between change patterns in Dynamic Event Attributes

Relationships Between Change Patterns in Dynamic Event Attri...

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21st International Conference on Business Process Management (BPM)

作者： Cremerius, Jonas Patzlaff, Hendrik Weske, Mathias Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783031509735;9783031509742

Process mining utilizes process execution data to discover and analyse business processes. Event logs represent process execution data, providing information about activities executed in a process instance. In addition to generic event attributes like activity and timestamp, events might contain domain-specific attributes, such as a blood sugar measurement in a healthcare environment. Many of these values change during a typical process quite frequently. Hence, we refer to those as dynamic event attributes. change patterns can be derived from dynamic event attributes, describing if the attribute values change from one activity to another. However, change patterns can only be identified in an isolated manner, neglecting the chance of finding co-occuring change patterns. This paper provides an approach to identify relationships between change patterns. We applied the proposed technique on the MIMIC-IV real-world dataset on hospitalizations in the US and evaluated the results with a medical expert. The approach is implemented in Python using the PM4Py framework.

关键词： Process Mining change pattern detection Relationships

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Data-Driven Approaches for Spatio-Temporal Analysis:A Survey of the State-of-the-Arts

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Journal of Computer Science & Technology 2020年第3期35卷 665-696页

作者： Monidipa Das Soumya K.Ghosh School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Computer Science and Engineering Indian Institute of Technology KharagpurKharagpur 721302India

With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal *** huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST *** this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data *** focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various *** start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary *** with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application *** anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

关键词： data-driven modeling spatio-temporal data prediction change pattern detection outlier detection hotspot detection partitioning/summarization (tele-)coupling visual analytics

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Mining the changes of Medical Behaviors for Clinical Pathways

Mining the Changes of Medical Behaviors for Clinical Pathway...

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14th World Congress on Medical and Health Informatics (MEDINFO)

作者： Huang, Zhengxing Gan, Chenxi Lu, Xudong Duan, Huilong Zhejiang Univ Coll Biomed Engn & Instrument Sci Key Lab Biomed Engn Minist Educ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781614992899;9781614992882

In a fast-changing healthcare environment, understanding the changes of medical behaviors in clinical pathways can help hospital managers improve the pathways and make better medical strategies for patient careflow. In this study we propose an approach to detect medical behavior changes between two time periods, by providing a change pattern detection algorithm dividing the discovered change patterns into four categories (i.e., perished patterns, added patterns, unexpected changes, and emerging patterns). The proposed approach is evaluated via real-world data sets extracted from Zhejiang Huzhou Central Hospital of China with regard to the clinical pathway of bronchial lung cancer in 2007-2009 and 2011. The experiment results include three categories of change patterns from the collected data-sets, making a relatively comprehensive cover on the significant changes in clinical pathways, which might be essential from the perspectives of clinical pathway analysis and improvement.

关键词： Clinical Pathway Analysis pattern Mining Emerging pattern change pattern detection

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