Toward automatic facet analysis and need negotiation

Lin, Jimmy; Wu, Philip Fei; Abels, Eileen G.

doi:10.1145/1416950.1416956

Cited by 4 publications

(3 citation statements)

References 80 publications

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“…Additionally, citation searches within these articles provided an additional 45 references [16, 22, 25–27, 30–34, 37, 63–102]. These 74 articles served as the basis for the development of the search query.…”

Section: Methodsmentioning

confidence: 99%

“…When users are aware of the reference interview’s purpose, they are willing to provide additional information on objective and intent. In a related study, Lin et al analyzed need negotiations and extracted a taxonomy of clarification questions that fit within a set of six classes [102]. The taxonomy applied in the context of EHR data interrogation is illustrated in Table 3, with example clarification questions.…”

Section: Channelmentioning

confidence: 99%

See 1 more Smart Citation

Facilitating biomedical researchers’ interrogation of electronic health record data: Ideas from outside of biomedical informatics

Hruby

Matsoukas

Cimino

et al. 2016

Journal of Biomedical Informatics

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Electronic health records (EHR) are a vital data resource for research uses, including cohort identification, phenotyping, pharmacovigilance, and public health surveillance. To realize the promise of EHR data for accelerating clinical research, it is imperative to enable efficient and autonomous EHR data interrogation by end users such as biomedical researchers. This paper surveys state-of-art approaches and key methodological considerations to this purpose. We adapted a previously published conceptual framework for interactive information retrieval, which defines three entities: user, channel, and source, by elaborating on channels for query formulation in the context of facilitating end users to interrogate EHR data. We show the current progress in biomedical informatics mainly lies in support for query execution and information modeling, primarily due to emphases on infrastructure development for data integration and data access via self-service query tools, but has neglected user support needed during iteratively query formulation processes, which can be costly and error-prone. In contrast, the information science literature has offered elaborate theories and methods for user modeling and query formulation support. The two bodies of literature are complementary, implying opportunities for cross-disciplinary idea exchange. On this basis, we outline the directions for future informatics research to improve our understanding of user needs and requirements for facilitating autonomous interrogation of EHR data by biomedical researchers. We suggest that cross-disciplinary translational research between biomedical informatics and information science can benefit our research in facilitating efficient data access in life sciences.

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Section: Methodsmentioning

confidence: 99%

Section: Channelmentioning

confidence: 99%

Facilitating biomedical researchers’ interrogation of electronic health record data: Ideas from outside of biomedical informatics

Hruby

Matsoukas

Cimino

et al. 2016

Journal of Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…More recently, from a system perspective, researchers have presented a case study that specifically used the systematic feedback of a trained intermediary for enhancing search results in an IR user experiment (Lin, Wu, & Abels, 2008). The results suggest that mediated searching can significantly improve search results, with appropriate question clarification strategies and restricted interactions made available to the intermediary.…”

Section: Related Workmentioning

confidence: 99%

On intermediaries' inquiring minds, elicitation styles, and user satisfaction

Liu

2011

J. Am. Soc. Inf. Sci.

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Building upon previous research on the concepts of inquiring minds and elicitation styles (Wu, 2005;Wu & Liu, 2003), this study aims to identify the relationships between the theoretical constructs of elicitation behavior and user satisfaction in terms of the relevance, utility, and satisfaction of search results, search interaction processes, and overall search activities. Descriptive statistical analysis is applied to compare the user satisfaction ratings with respect to the concepts of inquiring minds and elicitation styles. The results suggest that the stereotyped elicitation style received the lowest user satisfaction ratings compared with functionally and situationally oriented styles. It is suggested that the intermediaries take into account the characteristics of search questions and, accordingly, adapt their professional mindsets to search interview situations; that is, using an inquiring mind in the query formulation process as default mode with functional and situational styles of elicitations would be helpful for enhancing the user's satisfaction ratings. Future research is suggested to better understand and to improve professional talk in information services.

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CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks

et al. 2014

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BackgroundPredication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.ResultsWe developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.ConclusionsFor network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users.

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Toward automatic facet analysis and need negotiation

Cited by 4 publications

References 80 publications

Facilitating biomedical researchers’ interrogation of electronic health record data: Ideas from outside of biomedical informatics

Facilitating biomedical researchers’ interrogation of electronic health record data: Ideas from outside of biomedical informatics

On intermediaries' inquiring minds, elicitation styles, and user satisfaction

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks

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