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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 [...]

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.

The Health Insurance Portability and Accountability Act Privacy Rule enables healthcare organizations to share de-identified data via two routes. They can either 1) show re-identification risk is small (e.g., via a formal model, such as k-anonymity) with respect to an anticipated recipient or 2) apply a rule-based policy (i.e., Safe Harbor) that enumerates attributes to be altered (e.g., dates to years). The latter is often invoked because it is interpretable [...]

Electronic health data may improve the timeliness and accuracy of resource-intense contact investigations (CIs) in healthcare settings.

This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation.

To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.