Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs
Data Mining and Knowledge Discovery
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Approaching process mining with sequence clustering: experiments and findings
BPM'07 Proceedings of the 5th international conference on Business process management
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
An iterative requirements engineering framework based on Formal Concept Analysis and C-K theory
Expert Systems with Applications: An International Journal
Human-centered text mining: a new software system
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Process mining in healthcare: data challenges when answering frequently posed questions
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
Investigating clinical care pathways correlated with outcomes
BPM'13 Proceedings of the 11th international conference on Business Process Management
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Hospitals increasingly use process models for structuring their care processes. Activities performed to patients are logged to a database but these data are rarely used for managing and improving the efficiency of care processes and quality of care. In this paper, we propose a synergy of process mining with data discovery techniques. In particular, we analyze a dataset consisting of the activities performed to 148 patients during hospitalization for breast cancer treatment in a hospital in Belgium. We expose multiple quality of care issues that will be resolved in the near future, discover process variations and best practices and we discover issues with the data registration system. For example, 25% of patients receiving breast-conserving therapy did not receive the key intervention "revalidation". We found this was caused by lowering the length of stay in the hospital over the years without modifying the care process. Whereas the process representations offered by Hidden Markov Models are easier to use than those offered by Formal Concept Analysis, this data discovery technique has proven to be very useful for analyzing process anomalies and exceptions in detail.