Search (10 results, page 1 of 1)

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Abstract

Research in clinical information retrieval has long been stymied by the lack of open resources. However, both clinical information retrieval research innovation and legitimate privacy concerns can be served by the creation of intrainstitutional, fully protected resources. In this article, we provide some principles and tools for information retrieval resource-building in the unique problem setting of patient-level information retrieval, following the tradition of the Cranfield paradigm. We further include an analysis of parallel information retrieval resources at Oregon Health & Science University and Mayo Clinic that were built on these principles.

Footnote

Beitrag in einem Special issue on biomedical information retrieval.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.11, S.2636-2648

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Abstract

Online health platforms play an important role in chronic disease management. Patients participate in online health platforms to receive and provide health-related support from each other. However, there remains a debate about whether the influence of social interaction on patient health anxiety is linearly positive. Based on uncertainty, information overload, and the theory of motivational information management, we develop and test a model considering a potential curvilinear relationship between social interaction and health anxiety, as well as a moderating effect of health literacy. We collect patient interaction data from an online health platform based on chronic disease management in China and use text mining and econometrics to test our hypotheses. Specifically, we find an inverted U-shaped relationship between informational provision and health anxiety. Our results also show that information receipt and emotion provision have U-shaped relationships with health anxiety. Interestingly, health literacy can effectively alleviate the U-shaped relationship between information receipt and health anxiety. These findings not only provide new insights into the literature on online patient interactions but also provide decision support for patients and platform managers.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.11, S.1243-1264

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Source

SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a

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Abstract

Although it is commonly expected that the citation context of a reference is likely to provide more detailed and direct information about the nature of a citation, few studies in the literature have specifically addressed the extent to which the information in different parts of a scientific publication differs. Do abstracts tend to use conceptually broader terms than sentences in a citation context in the body of a publication? In this article, we propose a method to analyze and compare latent topics in scientific publications, in particular, from abstracts of papers that cited a target reference and from sentences that cited the target reference. We conducted an experiment and applied topical modeling techniques to full-text papers in eight biomedicine journals. Topics derived from the two sources are compared in terms of their similarities and broad-narrow relationships defined based on information entropy. The results show that abstracts and citation contexts are characterized by distinct sets of topics with moderate overlaps. Furthermore, the results confirm that topics from abstracts of citing papers have broader terms than topics from citation contexts formed by citing sentences. The method and the findings could be used to enhance and extend the current methodologies for research evaluation and citation evaluation.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.627-639

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Abstract

Much literature has been written speculating upon how classification can be used in online catalogs to improve information retrieval. While some empirical studies have been done exploring whether the direct use of traditional classification schemes designed for a manual environment is effective and efficient in the online environment, none has manipulated these manual classifications in such a w ay as to take full advantage of the power of both the classification and computer. It has been suggested by some authors, such as Wajenberg and Drabenstott, that this power could be realized if the individual components of synthesized DDC numbers could be identified and indexed. This paper looks at the feasibility of automatically decomposing DDC synthesized numbers and the implications of such decomposition for information retrieval. Based on an analysis of the instructions for synthesizing numbers in the main class Arts (700) and all DDC Tables, 17 decomposition rules were defined, 13 covering the Add Notes and four the Standard Subdivisions. 1,701 DDC synthesized numbers were decomposed by a computer system called DND (Dewey Number Decomposer), developed by the author. From the 1,701 numbers, 600 were randomly selected fo r examination by three judges, each evaluating 200 numbers. The decomposition success rate was 100% and it was concluded that synthesized DDC numbers can be accurately decomposed automatically. The study has implications for information retrieval, expert systems for assigning DDC numbers, automatic indexing, switching language development, enhancing classifiers' work, teaching library school students, and providing quality control for DDC number assignments. These implications were explored using a prototype retrieval system.

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Abstract

A model system, the Dewey Online Retrieval System (DORS), was implemented as an interface to an online catalog for the purpose of experimenting with classification-based search strategies and generally seeking further understanding of the role of traditional classifications in automated information retrieval. Specifications for a classification retrieval interface were enumerated and rationalized and the system was developed in accordance with them. The feature that particularly distinguishes the system and enables it to meet its stated specifications is an automatically generated chain index

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Abstract

With a long history, cataloging has evolved with changes in society, economy, and technology in China. This paper presents Chinese cataloging history in four parts, with emphasis on the last two parts: the founding of the People's Republic of China in 1949 and the development of cataloging after 1979 when China opened its doors to the world. Particularly important has been the rapid growth of online cataloging in recent years. The China Academic Library and Information System (CALIS), as a successful online cataloging model, is emphasized. Through investigation of the entire history of Chinese cataloging, three distinct features can be stated: (1) Standardization- switching from the Chinese traditional way to aligning with international standards, (2) Cooperation-from decentralized and self-supporting systems to sharing systems, (3) Computerization and networking-from manual operation to computer-based online operation. At the end of this paper, a set of means by which to enhance online cataloging and resource sharing is suggested.

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Abstract

Some empirical studies have explored the direct use of traditional classification schemes in the online environment; none has manipulated these manual classifications in such a way as to take full advantage of the power of both the classification and computer. It has been suggested that this power could be realized if the individual components of synthesized DDC numbers could be identified and indexed. Looks at the feasibility of automatically decomposing DDC synthesized numbers and the implications of such decompositions for informational retrieval. 1.701 sythesized numbers were decomposed by a computer system called DND (Dewey Number Decomposer). 600 were randomly selected for examination by 3 judges, each evaluating 200 numbers. The decomposition success rate was 100% and it was concluded that synthesized DDC numbers can be accurately decomposed automatically. The study has implications for information retrieval, expert systems for assigning DDC numbers, automatic indexing, switching language development and other important areas of cataloguing and classification

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Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1232-1243

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Abstract

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.