An annotated association mining approach for extracting and visualizing interesting clinical events

Objective: This work aims at deriving interesting clinical events using association rule mining based on a user-annotated order of clinical features. Materials and methods: A user specifies a partial temporal order of features by indexing features of interest, with repeated and bundled indexes allowed as needed. An association mining algorithm plugin was designed to generate rules that adhere to the user-specified temporal order. The plugin uses temporal and sequence constraints to reduce rule permutations early in the rule generation process. The method was evaluated with a large medical claims dataset to generate clinical events. Results: Using the plug-in algorithm, the database is scanned to calculate the support of item sequences whose sequential order conforms with the user annotated feature order. In our experiments with 20,000 medical claim data records, our method generated rules in a significantly less time than the standalone Apriori algorithm. Our approach generates dendrograms to organize the rules into meaningful hierarchies and provides a graphical interface to navigate the rules and unfold interesting clinical events. Discussion: Since many associations in healthcare are of sequential nature, some of the derived rules may describe interesting clinical flows or events, while others may be contextually irrelevant. Our method exploits user-specified sequence constraints to eliminate irrelevant rules and reduce rule permutations, speeding up rule mining. Conclusion: This work can be the foundation for future association rule mining studies to extract sequential events based on interestingness. The work can support clinical education where the instructor defines feature sequence constraints, and students unfold and examine extracted sequential rules.