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To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports.

To present a framework for combining implicit knowledge acquisition from multiple experts with machine learning and to evaluate this framework in the context of anemia alerts.

Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text.

To describe an analytical framework for quantifying the societal savings and financial consequences of a health information exchange (HIE), and to demonstrate its use in designing pricing policies for sustainable HIEs.

OBJECTIVES: Clinical decision support (CDS), such as computerized alerts, improves prescribing in the setting of acute kidney injury (AKI), but considerable opportunity remains to improve patient safety. The authors sought to determine whether pharmacy surveillance of AKI patients could detect and prevent medication errors that are not corrected by automated interventions. METHODS: The authors conducted a randomized clinical trial among 396 patients admitted to an academic, tertiary care hospital between [...]

This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity.