Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

Background: Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback.

Methods: The system employed association mining techniques to build a k-profile representing a user's relevance feedback.

A functional temporal association mining approach for screening potential drug-drug interactions from electronic patient databases.

Aims: Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on spontaneous reports. These methods suffer from severe underreporting, incompleteness, and various bias.

Patient care complexity as perceived by primary care physicians.

Introduction: Currently there are various definitions of patient care complexity with little consensus. The numbers of patients with complex care needs are increasing. To improve interventions for "complex patients" and appropriately reimburse healthcare providers it is important to determine the characteristics or contextual factors contributing to complexity.

A temporal interestingness measure for drug interaction signal detection in post-marketing surveillance.

Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on the FDA's (Food and Drug Administration) spontaneous reporting system. However, this system suffers from severe underreporting, which makes it difficult to timely collect enough valid cases for statistical analysis.

A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance.

Early detection of unknown adverse drug reactions (ADRs) in postmarketing surveillance saves lives and prevents harmful consequences. We propose a novel data mining approach to signaling potential ADRs from electronic health databases. More specifically, we introduce potential causal association rules (PCARs) to represent the potential causal relationship between a drug and ICD-9 (CDC.