AI Article Synopsis
- Mining multiple drug interactions and reactions from the Adverse Event Reporting System (AERS) is a complex problem that hasn't been fully solved by current methods.
- The proposed FCI-filter approach combines UMLS mapping, closed itemset mining, and the removal of uninformative associations to identify significant drug interactions.
- Statistical analysis of the results shows that identified associations have low p-values, indicating their significance, and suggests that integrating external knowledge could enhance the method further.
Article Abstract
Efficiently mining multiple drug interactions and reactions from Adverse Event Reporting System (AERS) is a challenging problem which has not been sufficiently addressed by existing methods. To tackle this challenge, we propose a FCI-fliter approach which leverages the efforts of UMLS mapping, frequent closed itemset mining, and uninformative association identification and removal. By applying our method on AERS, we identified a large number of multiple drug interactions with reactions. By statistical analysis, we found most of the identified associations have very small p-values which suggest that they are statistically significant. Further analysis on the results shows that many multiple drug interactions and reactions are clinically interesting, and suggests that our method may be further improved with the combination of external knowledge.
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