_:n8798c06f26af4e279b9770aef7c41418xb49
<http://schema.org/description>
"Trace clustering has increasingly been applied to find homogenous process\nexecutions. However, current techniques have difficulties in finding a\nmeaningful and insightful clustering of patients on the basis of healthcare\ndata. The resulting clusters are often not in line with those of medical\nexperts, nor do the clusters guarantee to help return meaningful process maps\nof patients' clinical pathways. After all, a single hospital may conduct\nthousands of distinct activities and generate millions of events per year. In\nthis paper, we propose a novel trace clustering approach by using sample sets\nof patients provided by medical experts. More specifically, we learn frequent\nsequence patterns on a sample set, rank each patient based on the patterns, and\nuse an automated approach to determine the corresponding cluster. We find each\ncluster separately, while the frequent sequence patterns are used to discover a\nprocess map. The approach is implemented in ProM and evaluated using a large\ndata set obtained from a university medical center. The evaluation shows\nF1-scores of 0.7 for grouping kidney injury, 0.9 for diabetes, and 0.64 for\nhead/neck tumor, while the process maps show meaningful behavioral patterns of\nthe clinical pathways of these groups, according to the domain experts."
<https://deepai.org/profile/mark-hoogendoorn>