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We have identified 5 use cases that comprise a useful definition of an "open or interoperable electronic health record (EHR)." Each of these use cases represents important functionality that should be available to 1) clinicians, so they can provide safe and effective health care; 2) researchers, so they can advance our understanding of disease and health care processes; 3) administrators, so they can reduce their reliance on a single-source EHR [...]

Semantic role labeling (SRL), which extracts a shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding natural language. Few studies in SRL have been conducted in the medical domain, primarily due to lack of annotated clinical SRL corpora, which are time-consuming and costly to build. The goal of this study is to investigate domain adaptation techniques for clinical SRL leveraging resources built [...]

Automatically identifying specific phenotypes in free-text clinical notes is critically important for the reuse of clinical data. In this study, the authors combine expert-guided feature (text) selection with one-class classification for text processing.

An individual's birth month has a significant impact on the diseases they develop during their lifetime. Previous studies reveal relationships between birth month and several diseases including atherothrombosis, asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. This retrospective population study systematically explores the relationship between seasonal affects at birth and lifetime disease risk for 1688 conditions.

To develop and test an instrument for assessing a healthcare organization's ability to mitigate malpractice risk through clinical decision support (CDS).

Many tasks in natural language processing utilize lexical pattern-matching techniques, including information extraction (IE), negation identification, and syntactic parsing. However, it is generally difficult to derive patterns that achieve acceptable levels of recall while also remaining highly precise.

Literature-based discovery (LBD) aims to identify "hidden knowledge" in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of [...]

To create a multilingual gold-standard corpus for biomedical concept recognition.

Evidence supports the potential for e-prescribing to reduce the incidence of adverse drug events (ADEs) in hospital-based studies, but studies in the ambulatory setting have not used occurrence of ADE as their outcome. Using the "prescription origin code" in 2011 Medicare Part D prescription drug events files, the authors investigate whether physicians who meet the meaningful use stage 2 threshold for e-prescribing (≥50% of prescriptions e-prescribed) have lower rates of [...]