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Binge eating disorder (BED) does not have an International Classification of Diseases, 9th or 10th edition code, but is included under 'eating disorder not otherwise specified' (EDNOS). This historical cohort study identified patients with clinician-diagnosed BED from electronic health records (EHR) in the Department of Veterans Affairs between 2000 and 2011 using natural language processing (NLP) and compared their characteristics to patients identified by EDNOS diagnosis codes. NLP identified 1487 [...]

To create a clinical decision support (CDS) system that is shareable across healthcare delivery systems and settings over large geographic regions.

The aim of this paper is to report on the use of the systematised nomenclature of medicine clinical terms (SNOMED CT) by providing an overview of published papers.

To study and report on the use of open source electronic health records (EHR) to assist with chronic care management within safety net medical settings, such as community health centers (CHC).

To determine the accuracy of vendor-supplied dosing eRules for pediatric medication orders. Inaccurate or absent dosing rules can lead to high numbers of false alerts or undetected prescribing errors and may potentially compromise safety in this already vulnerable population.

To evaluate dosing alert appropriateness, categorize orders with alerts, and compare the appropriateness of alerts due to customized and non-customized dose ranges at a pediatric hospital.

To review the literature on the views of healthcare professionals to the linkage of healthcare data and to identify any potential barriers and/or facilitators to participation in a data linkage system.

There is little evidence that readability formula outcomes relate to text understanding. The potential cause may lie in their strong reliance on word and sentence length. We evaluated word familiarity rather than word length as a stand-in for word difficulty. Word familiarity represents how well known a word is, and is estimated using word frequency in a large text corpus, in this work the Google web corpus. We conducted a [...]

Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time.