Rethinking information delivery: using a natural language processing application for point-of-care data discovery

Workman, T. Elizabeth; Stoddart, Joan M.

doi:10.3163/1536-5050.100.2.009

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…System performance for prevention had similar results, with recall ranging from 0.50 to 0.76, with overall fewer citations than the drug treatment data. However, in a pilot project the system produced 100% recall for prevention data on a single disease topic (acute pancreatitis), with only 156 citations [ 41 ]. We conclude that citation volume can be a factor for some clinical topics, but not for all of them.…”

Section: Discussionmentioning

confidence: 99%

“…In this current study, we examined dynamic summarization for just four disease topics. However, a pilot project [ 41 ] featuring three different disease topics (acute pancreatitis, coronary artery disease, and malaria), again within the context of preventive intervention decision support, produced slightly superior results. This creates optimism that this text summarization method may enable others to locate decision support data.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to predications in the form “Intervention X_PREVENTS_disease_Y,” other predications where argument concepts had prevention terms such as “Exercise, aerobic_AFFECTS_blood pressure” and “Primary Prevention_USES_Metformin” were used, because their value was confirmed in a previous study [41]. …”

Section: Methodsmentioning

confidence: 99%

“…Only the Combo algorithm summarized output for the four disease topic/prevention and control pairings was compared to the reference standard, since there is no conventional schema for prevention. In addition to predications in the form “Intervention X_PREVENTS_disease_Y,” other predications where argument concepts had prevention terms such as “Exercise, aerobic_AFFECTS_blood pressure” and “Primary Prevention_USES_Metformin” were used, because their value was confirmed in a previous study [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

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Text summarization as a decision support aid

Workman

Fiszman

Hurdle

2012

BMC Med Inform Decis Mak

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BackgroundPubMed data potentially can provide decision support information, but PubMed was not exclusively designed to be a point-of-care tool. Natural language processing applications that summarize PubMed citations hold promise for extracting decision support information. The objective of this study was to evaluate the efficiency of a text summarization application called Semantic MEDLINE, enhanced with a novel dynamic summarization method, in identifying decision support data.MethodsWe downloaded PubMed citations addressing the prevention and drug treatment of four disease topics. We then processed the citations with Semantic MEDLINE, enhanced with the dynamic summarization method. We also processed the citations with a conventional summarization method, as well as with a baseline procedure. We evaluated the results using clinician-vetted reference standards built from recommendations in a commercial decision support product, DynaMed.ResultsFor the drug treatment data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.848 and 0.377, while conventional summarization produced 0.583 average recall and 0.712 average precision, and the baseline method yielded average recall and precision values of 0.252 and 0.277. For the prevention data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.655 and 0.329. The baseline technique resulted in recall and precision scores of 0.269 and 0.247. No conventional Semantic MEDLINE method accommodating summarization for prevention exists.ConclusionSemantic MEDLINE with dynamic summarization outperformed conventional summarization in terms of recall, and outperformed the baseline method in both recall and precision. This new approach to text summarization demonstrates potential in identifying decision support data for multiple needs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Text summarization as a decision support aid

Workman

Fiszman

Hurdle

2012

BMC Med Inform Decis Mak

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“…In addition, some work attempts to use automated approaches either to approximate human language use (e.g. Workman & Stoddart, 2012) or to develop systems to automatically interpret and modify users' system queries (e.g. Symonds et.…”

Section: The Domain Of Significationmentioning

confidence: 99%

Information Worlds and Interpretive Practices: Toward an Integration of Domains

Burnett¹

2015

Journal of Information Science Theory and Practice

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This article proposes an interwoven three-part framework for conceptualizing and analyzing the role of information in human activities, melding the cognitive and affective domain of the individual, the collective domain of the social, and the domain of signification and communication practices, focusing on the ways in which individual characteristics, social context and interaction, and signification and representation work together to form information behavior. The article presents an overview of each of these three domains and discusses the ways in which they are intertwined. It argues that considering the three domains in relation to each other offers a holistic framework within which to consider the ways in which information -needs, behavior, creation, and use -depends simultaneously on all three. It concludes by offering a brief discussion of the implications of the framework for information services, including (but not limited to) libraries.

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