Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
The Journal of Machine Learning Research
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IT support for healthcare processes - premises, challenges, perspectives
Data & Knowledge Engineering
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Unsupervised context detection using wireless signals
Pervasive and Mobile Computing
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Mining process execution and outcomes: position paper
BPM'07 Proceedings of the 2007 international conference on Business process management
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
Measuring clinical pathway adherence
Journal of Biomedical Informatics
Process discovery in event logs: An application in the telecom industry
Applied Soft Computing
Variation prediction in clinical processes
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Artificial Intelligence in Medicine
Flexible guideline-based patient careflow systems
Artificial Intelligence in Medicine
Using Recommendation to Support Adaptive Clinical Pathways
Journal of Medical Systems
Supporting adaptive clinical treatment processes through recommendations
Computer Methods and Programs in Biomedicine
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
Summarizing clinical pathways from event logs
Journal of Biomedical Informatics
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Discovery of clinical pathway (CP) patterns has experienced increased attention over the years due to its importance for revealing the structure, semantics and dynamics of CPs, and to its usefulness for providing clinicians with explicit knowledge which can be directly used to guide treatment activities of individual patients. Generally, discovery of CP patterns is a challenging task as treatment behaviors in CPs often have a large variability depending on factors such as time, location and patient individual. Based on the assumption that CP patterns can be derived from clinical event logs which usually record various treatment activities in CP executions, this study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs. Clinical case studies are performed to evaluate the proposed approach via real-world data sets recording typical treatment behaviors in patient careflow. The obtained results demonstrate the suitability of the proposed approach for CP pattern discovery, and indicate the promise in research efforts related to CP analysis and optimization.