Background Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality.Methods A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed.Results Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025).Conclusions Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall).

The presence of a phrase boundary is often marked in speech by phrase-final lengthening-a lengthening of the final stressed syllable of the phrase and pause at the phrase boundary. The present study investigates (a) whether listeners use the feature of phrase-final lengthening to parse syntactically ambiguous sentences such as "Kate or Pat and Tony will come," where the position of a phrase boundary after "Kate" represents one meaning, and after "Pat" another meaning, and (b) whether listeners use phrase-final lengthening directly to parse the sentence or indirectly via the effect that phrase-final lengthening has on the rhythm of the feet (the onsets of the stressed syllables) of the sentence. Four experiments are reported in which listeners are asked to judge the meaning of sentences which have been temporally manipulated so that the foot which originally did not contain the crucial phrase boundary is lengthened by (i) inserting a pause at the "false" phrase boundary [experiment I], (ii) inserting a pause and lengthening the final stressed syllable at the "false" phrase boundary [experiments II], (iii) lengthening all segments contained in the foot [experiment III], and (iv) lengthening only the conjunction within the foot [experiment IV]. The results indicate that both rhythm of the inter-stress intervals and the presence of phrase-final lengthening influence listeners' perception of a phrase boundary, although the stress rhythm appears to be the more powerful perceptual cue.

Recent research in speech production has shown that syntactic structure is reflected in segmental phonology--the application of certain phonological rules of English (e.g., palatalization and alveolar flapping) is inhibited across phrase boundaries. We examined whether such segmental effects can be used in speech perception as cues to syntactic structure, and the relation between the use of these segmental features as syntactic markers in production and perception. Speakers of American English (a dialect in which the above segmental effects occur) could indeed use the segmental cues in syntax perception; speakers of British English (in which the effects do not occur) were unable to make use of them, while speakers of British English who were long-term residents of the United States showed intermediate performance.Anyone who has had to make the separate meanings of an ambiguous sentence clear to listeners will know that often the most efficient method is just to say the sentence in different ways. Of course, some sentences will be more difficult to disambiguate in this manner than others; in general, surface structure ambiguities (e.g., "The old men and women stayed at home") prove easier than deep structure ambiguities (e.g., "Flying planes can be

We argue the case for abstract document structure as a separate descriptive level in the analysis and generation of written texts. The purpose of this representation is to mediate between the message of a text (i.e., its discourse structure) and its physical presentation (i.e., its organization into graphical constituents like sections, paragraphs, sentences, bulleted lists, figures, and footnotes). Abstract document structure can be seen as an extension of Nunberg's “text-grammar” it is also closely related to “logical” markup in languages like HTML and LaTEX. We show that by using this intermediate representation, several subtasks in language generation and language understanding can be defined more cleanly.

We present the rags (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.