Search (5 results, page 1 of 1)

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Abstract

The explosion of disaster health information results in information overload among response professionals. The objective of this project was to determine the feasibility of applying semantic natural language processing (NLP) technology to addressing this overload. The project characterizes concepts and relationships commonly used in disaster health-related documents on influenza pandemics, as the basis for adapting an existing semantic summarizer to the domain. Methods include human review and semantic NLP analysis of a set of relevant documents. This is followed by a pilot test in which two information specialists use the adapted application for a realistic information-seeking task. According to the results, the ontology of influenza epidemics management can be described via a manageable number of semantic relationships that involve concepts from a limited number of semantic types. Test users demonstrate several ways to engage with the application to obtain useful information. This suggests that existing semantic NLP algorithms can be adapted to support information summarization and visualization in influenza epidemics and other disaster health areas. However, additional research is needed in the areas of terminology development (as many relevant relationships and terms are not part of existing standardized vocabularies), NLP, and user interface design.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2531-2543

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Abstract

Since millions seek health information online, it is vital for this information to be comprehensible. Most studies use readability formulas, which ignore vocabulary, and conclude that online health information is too difficult. We developed a vocabularly-based, naïve Bayes classifier to distinguish between three difficulty levels in text. It proved 98% accurate in a 250-document evaluation. We compared our classifier with readability formulas for 90 new documents with different origins and asked representative human evaluators, an expert and a consumer, to judge each document. Average readability grade levels for educational and commercial pages was 10th grade or higher, too difficult according to current literature. In contrast, the classifier showed that 70-90% of these pages were written at an intermediate, appropriate level indicating that vocabulary usage is frequently appropriate in text considered too difficult by readability formula evaluations. The expert considered the pages more difficult for a consumer than the consumer did.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1409-1419

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Abstract

We describe the use of a domain-independent method to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®; Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. Natural language processing and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed® searches in this field of knowledge.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.1963-1974

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Abstract

The predominance of English-only online health information poses a serious challenge to nonEnglish speakers. To overcome this barrier, we incorporated cross-language information retrieval (CLIR) techniques into a fully functional prototype. It supports Spanish language searches over an English data set using a Spanish-English bilingual term list (BTL). The modular design allows for system and BTL growth and takes advantage of English-system enhancements. Language-based design decisions and implications for integrating non-English components with the existing monolingual architecture are presented. Algorithmic and BTL improvements are used to bring CUR retrieval scores in line with the monolingual values. After validating these changes, we conducted a failure analysis and error categorization for the worst performing queries. We conclude with a comprehensive discussion and directions for future work.

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Abstract

This preliminary study applies a bilingual term list (BTL) approach to cross-language information retrieval (CLIR) in the consumer health domain and compares it to a machine translation (MT) approach. We compiled a Spanish-English BTL of 34,980 medical and general terms. We collected a training set of 466 general health queries from MedlinePlus en espaiiol and 488 domainspecific queries from ClinicalTrials.gov translated into Spanish. We submitted the training set queries in English against a test bed of 7,170 ClinicalTrials.gov English documents, and compared MT and BTL against this English monolingual standard. The BTL approach was less effective (F = 0.420) than the MT approach (F = 0.578). A failure analysis of the results led to substitution of BTL dictionary sources and the addition of rudimentary normalisation of plural forms. These changes improved the CLIR effectiveness of the same training set queries (F = 0.474), and yielded comparable results for a test set of new 954 queries (F= 0.484). These results will shape our efforts to support Spanishspeakers' needs for consumer health information currently only available in English.

Source

Knowledge organization and the global information society: Proceedings of the 8th International ISKO Conference 13-16 July 2004, London, UK. Ed.: I.C. McIlwaine