Academic journal article Journal of the Medical Library Association

Rethinking Information Delivery: Using a Natural Language Processing Application for Point-of-Care Data Discovery*[dagger]

Academic journal article Journal of the Medical Library Association

Rethinking Information Delivery: Using a Natural Language Processing Application for Point-of-Care Data Discovery*[dagger]

Article excerpt

Objective: This paper examines the use of Semantic MEDLINE, a natural language processing application enhanced with a statistical algorithm known as Combo, as a potential decision support tool for clinicians. Semantic MEDLINE summarizes text in PubMed citations, transforming it into compact declarations that are filtered according to a user's information need that can be displayed in a graphic interface. Integration of the Combo algorithm enables Semantic MEDLINE to deliver information salient to many diverse needs.

Methods: The authors selected three disease topics and crafted PubMed search queries to retrieve citations addressing the prevention of these diseases. They then processed the citations with Semantic MEDLINE, with the Combo algorithm enhancement. To evaluate the results, they constructed a reference standard for each disease topic consisting of preventive interventions recommended by a commercial decision support tool.

Results: Semantic MEDLINE with Combo produced an average recall of 79% in primary and secondary analyses, an average precision of 45%, and a final average F-score of 0.57.

Conclusion: This new approach to point-of-care information delivery holds promise as a decision support tool for clinicians. Health sciences libraries could implement such technologies to deliver tailored information to their users.


Clinicians often encounter information needs in their work of caring for patients. In their 2005 study, Ely and colleagues discovered that physicians developed an average of 5.5 questions for each half-day observation, yet could not find answers to 41% of the questions for which they pursued answers [I]. Ely cited time constraints as one of the barriers preventing clinicians from finding answers. In another study, Chambliss and Conley also found that discovering answers is excessively time consuming [2].

Chambliss and Conley determined that references found in MEDLINE could answer or nearly answer 71% of clinicians' answerable questions; however, PubMed is not a tool exclusively designed for pointof-care information delivery. It generally returns excessive, irrelevant data, even when implementing diverse search strategies [S]. Clinicians can spend an average of 30 minutes answering a question using references from MEDLINE [4]. This time span is, by and large, due to the process of literature appraisal, which is naturally lengthened by excessive retrieval [5]. This information discovery process is not practical for a busy clinical setting [4].

Semantic MEDLINE

Natural language processing (NLP) applications such as Semantic MEDLINE can filter PubMed results for a user's specific information need and summarize them to facilitate literature appraisal [6]. Semantic MEDLINE}, a resource developed by the National Library of Medicine (NLM), if enhanced by an adaptive algorithm known as Combo [7], can simplify MEDLINE results for many information needs. The user activates the Semantic MEDLEME application by submitting a search query expressing an information need to PubMed. Semantic MEDLINE then uses the individual processes of SemRep, Summarization, and Visualization to quickly transform the citations' title and abstract text into a compact form and identify data that are salient to a specific information need, which then can be displayed in a visual graph. Currently, NLM hosts an online Semantic MEDLINE application that consists of a publicly accessible demonstration site and a restricted-access portal. The following paper describes these individual processes. This study evaluated a separate, enhanced Semantic MEDLINE system that accommodates additional information needs. This paper also briefly describes how an organization could develop it to serve its own users.


SemRep [8], a rule-based NLP application in Semantic MEDLINE, interprets the meaning of PubMed title and abstract text and rephrases it into compact declarations called semantic predications. …

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