Browsing by Subject "Information Storage and Retrieval"

Now showing 1 - 4 of 4
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    Autism Spectrum Disorders: Wading through the controversies on the web
    (Taylor & Francis, 2009) Coates, Heather L.
    Autism is one of three developmental disorders in the group known as the autism spectrum disorders (ASDs). This spectrum of disorders has an estimated prevalence of one in 150 children. Increased awareness and diagnosis has led to an explosion of information available about the disorder. This explosion has made scientific research more readily available, along with inaccurate and spurious information. Autism is a disorder without a known cause or cure and few treatments with sufficient evidence to indicate effectiveness. Due to the variable presentation of autism, there is no single intervention that is effective for all individuals. The complexity of the disorder is addressed by research and practice across several disciplines, including education, psychology, psychiatry, neurology, genetics, and internal medicine. This resource guide will introduce the range of autism spectrum disorders, its various perspectives and treatments, and will point librarians and patrons to introductory resources to provide links for further learning.
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    Automatic Export of PubMed Citations to EndNote
    (http://www.tandfonline.com/doi/full/10.1080/02763861003723317#.UnARCCQ2-CY, 2010-04) London, Sue; Gurdal, Osman; Gall, Carole
    The export of MEDLINE references to EndNote can be accomplished in various ways. Unlike Ovid MEDLINE, PubMed does not have a direct export feature to EndNote. Until recently, PubMed references had to be saved as a text file to import into EndNote. Now, the automatic export of PubMed references can be done using Internet Explorer (IE) or Mozilla Firefox Web browsers. The development and teaching of seamless citation management is a value-added service to health professionals.
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    Learning from the crowd while mapping to LOINC
    (Oxford University Press, 2015-11) Vreeman, Daniel J.; Hook, John; Dixon, Brian E.; Department of Medicine, IU School of Medicine
    OBJECTIVE: To describe the perspectives of Regenstrief LOINC Mapping Assistant (RELMA) users before and after the deployment of Community Mapping features, characterize the usage of these new features, and analyze the quality of mappings submitted to the community mapping repository. METHODS: We evaluated Logical Observation Identifiers Names and Codes (LOINC) community members' perceptions about new "wisdom of the crowd" information and how they used the new RELMA features. We conducted a pre-launch survey to capture users' perceptions of the proposed functionality of these new features; monitored how the new features and data available via those features were accessed; conducted a follow-up survey about the use of RELMA with the Community Mapping features; and analyzed community mappings using automated methods to detect potential errors. RESULTS: Despite general satisfaction with RELMA, nearly 80% of 155 respondents to our pre-launch survey indicated that having information on how often other users had mapped to a particular LOINC term would be helpful. During the study period, 200 participants logged into the RELMA Community Mapping features an average of 610 times per month and viewed the mapping detail pages a total of 6686 times. Fifty respondents (25%) completed our post-launch survey, and those who accessed the Community Mapping features unanimously indicated that they were useful. Overall, 95.3% of the submitted mappings passed our automated validation checks. CONCLUSION: When information about other institutions' mappings was made available, study participants who accessed it agreed that it was useful and informed their mapping choices. Our findings suggest that a crowd-sourced repository of mappings is valuable to users who are mapping local terms to LOINC terms.
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    Menopause and Big Data: Word Adjacency Graph Modeling of Menopause-Related ChaCha® Data
    (Wolters Kluwer, 2017-07) Carpenter, Janet S.; Groves, Doyle; Chen, Chen X.; Otte, Julie L.; Miller, Wendy; School of Nursing
    OBJECTIVE: To detect and visualize salient queries about menopause using Big Data from ChaCha. METHODS: We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content. RESULTS: We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263,363 queries from individuals aged 13 and older and the second contained 5,892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment. CONCLUSIONS: We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.