TY - JOUR
T1 - Automatic Process Comparison for Subpopulations
T2 - Application in Cancer Care
AU - Marazza, Francesca
AU - Bukhsh, Faiza Allah
AU - Geerdink, Jeroen
AU - Vijlbrief, Onno
AU - Pathak, Shreyasi
AU - van Keulen, Maurice
AU - Seifert, Christin
N1 - Funding Information:
Acknowledgments: This work was supported by the Hospital Group Twente (ZGT) by providing data, secure server infrastructure and domain advice.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/8/7
Y1 - 2020/8/7
N2 - Processes in organisations, such as hospitals, may deviate from the intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models that were extracted from events in electronic health records. Concretely, we propose comparing processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. We perform a user study with 20 participants in order to obtain a ground truth for similarity of process models. We evaluate our approach on two data sets, the publicly available MIMIC database with the focus on different cancer patients in intensive care, and a database on breast cancer patients from a Dutch hospital. In our experiments, we found average fitness to be a good indicator for visual similarity in the ZGT use case, while the average precision and graph edit distance are strongly correlated with visual impression for cancer process models on MIMIC. These results are a call for further research and evaluation for determining which similarity or combination of similarities is needed in which type of process model comparison.
AB - Processes in organisations, such as hospitals, may deviate from the intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models that were extracted from events in electronic health records. Concretely, we propose comparing processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. We perform a user study with 20 participants in order to obtain a ground truth for similarity of process models. We evaluate our approach on two data sets, the publicly available MIMIC database with the focus on different cancer patients in intensive care, and a database on breast cancer patients from a Dutch hospital. In our experiments, we found average fitness to be a good indicator for visual similarity in the ZGT use case, while the average precision and graph edit distance are strongly correlated with visual impression for cancer process models on MIMIC. These results are a call for further research and evaluation for determining which similarity or combination of similarities is needed in which type of process model comparison.
KW - Breast cancer care
KW - Cancer types
KW - MIMIC database
KW - Process comparison
KW - Process mining
KW - Quality control
UR - http://www.scopus.com/inward/record.url?scp=85089405313&partnerID=8YFLogxK
U2 - 10.3390/ijerph17165707
DO - 10.3390/ijerph17165707
M3 - Article
SN - 1661-7827
VL - 17
SP - 1
EP - 23
JO - International journal of environmental research and public health
JF - International journal of environmental research and public health
IS - 16
M1 - 5707
ER -