Özet:

The objectives of this study were to structure otorhinolaryngology discharge summaries with text mining methods and analyze structured data and extract relational rules using Association Rule Mining (ARM). In this study, we used otorhinolaryngology discharge notes. We first developed a dictionary-based information extraction (IE) module in order to annotate medical entities. Later we extracted the annotated entities, and transformed all documents into a data table. We applied ARM Apriori algorithm to the final dataset, and identified interesting patterns and relationships between the entities as association rules for predicting the treatment procedure for patients. The IE module’s precision, recall, and f-measure were 95.1%, 84.5%, and 89.2%, respectively.  A total of fifteen association rules were found by selecting the top ranking rules obtained from the ARM analysis. These fifteen rules were reviewed by a domain expert, and the validity of these rules was examined in the PubMed literature. The results showed that the association rules are mostly endorsed by the literature. Although our system focuses on the domain of otorhinolaryngology, we believe the same methodology can be applied to other medical domains and extracted rules can be used for clinical decision support systems and in patient care.

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