Efficiently mining Adverse Event Reporting System for multiple drug interactions.

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.

Effectively processing medical term queries on the UMLS Metathesaurus by layered dynamic programming.

BACKGROUND: Mapping medical terms to standardized UMLS concepts is a basic step for leveraging biomedical texts in data management and analysis. However, available methods and tools have major limitations in handling queries over the UMLS Metathesaurus that contain inaccurate query terms, which frequently appear in real world applications. METHODS: To provide a practical solution for this task, we propose a layered dynamic programming mapping (LDPMap) approach, which can efficiently handle these queries. LDPMap uses indexing and two layers of dynamic programming techniques to efficiently map a biomedical term to a UMLS concept. RESULTS: Our empirical study shows that LDPMap achieves much faster query speeds than LCS. In comparison to the UMLS Metathesaurus Browser and MetaMap, LDPMap is much more effective in querying the UMLS Metathesaurus for inaccurately spelled medical terms, long medical terms, and medical terms with special characters. CONCLUSIONS: These results demonstrate that LDPMap is an efficient and effective method for mapping medical terms to the UMLS Metathesaurus.