Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Adept_flex—Supporting Dynamic Changes of Workflows Without Losing Control
Journal of Intelligent Information Systems - Special issue on workflow management systems
Workflow management: models, methods, and systems
Workflow management: models, methods, and systems
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
IT support for healthcare processes - premises, challenges, perspectives
Data & Knowledge Engineering
Computer Methods and Programs in Biomedicine
An ontology-based hierarchical semantic modeling approach to clinical pathway workflows
Computers in Biology and Medicine
Design patterns for clinical guidelines
Artificial Intelligence in Medicine
Constructing comprehensive summaries of large event sequences
ACM Transactions on Knowledge Discovery from Data (TKDD)
A new approach to systematization of the management of paper-based clinical pathways
Computer Methods and Programs in Biomedicine
Approaching process mining with sequence clustering: experiments and findings
BPM'07 Proceedings of the 5th international conference on Business process management
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
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
A Holistic Environment for the Design and Execution of Self-Adaptive Clinical Pathways
IEEE Transactions on Information Technology in Biomedicine
Flexible guideline-based patient careflow systems
Artificial Intelligence in Medicine
Using Recommendation to Support Adaptive Clinical Pathways
Journal of Medical Systems
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
Methodological Review: Computer-interpretable clinical guidelines: A methodological review
Journal of Biomedical Informatics
Length of stay prediction for clinical treatment process using temporal similarity
Expert Systems with Applications: An International Journal
Reprint of "Length of stay prediction for clinical treatment process using temporal similarity"
Expert Systems with Applications: An International Journal
Discovery of clinical pathway patterns from event logs using probabilistic topic models
Journal of Biomedical Informatics
Hi-index | 0.00 |
Objective: Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Research in clinical pathway analysis has so far mostly focused on looking at aggregated data seen from an external perspective, and only provide very limited insight into the pathways. In some recent work, process mining techniques have been studied in discovering clinical pathway models from data. While it is interesting, discovered models may provide too much detail to give a comprehensive summary of the pathway. Moreover, the number of patterns discovered can be large. Alternatively, this article presents a new approach to build a concise and comprehensive summary that describes the entire structure of a clinical pathway, while revealing essential/critical medical behaviors in specific time intervals over the whole time period of the pathway. Methods: The presented approach summarizes a clinical pathway from the collected clinical event log, which regularly records all kinds of patient therapy and treatment activities in clinical workflow by various hospital information systems. The proposed approach formally defines the clinical pathway summarization problem as an optimization problem that can be solved in polynomial time by using a dynamic-programming algorithm. More specifically, given an input event log, the presented approach summarizes the pathway by segmenting the observed time period of the pathway into continuous and overlapping time intervals, and discovering frequent medical behavior patterns in each specific time interval from the log. Results: The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to four specific diseases, i.e., bronchial lung cancer, colon cancer, gastric cancer, and cerebral infarction, in two years (2007.08-2009.09). Although the medical behaviors contained in these logs are very diverse and heterogeneous, experimental results indicates that the presented approach is feasible to construct condensed clinical pathway summaries in polynomial time from the collected logs, and have a linear scalability against the increasing size of the logs. Conclusion: Experiments on real data-sets illustrate that the presented approach is efficient and discovers high-quality results: the observed time period of a clinical pathway is correctly segmented into a set of continuous and overlapping time intervals, in which essential/critical medical behaviors are well discovered from the event log to form the backbone of a clinical pathway. The experimental results indicate that the generated clinical pathway summary not only reveals the global structure of a pathway, but also provides a thorough understanding of the way in which actual medical behaviors are practiced in specific time intervals, which might be essential from the perspectives of clinical pathway analysis and improvement.