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PLoS One. 2023;18(2):e0281422.

Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system: EEEvis.

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제1저자 이종찬, Brian J Lee, 박창희 · 교신저자 황진혁, 서진욱

소속 (6)
  1. Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
  2. College of Medicine, Seoul National University, Seoul, Korea.
  3. Department of Computer Science & Engineering, Seoul National University, Seoul, Korea.
  4. Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  5. School of Computer Science & Engineering, Soongsil University, Seoul, Korea.
  6. Bang & Ock Consulting, Inc., Seoul, Korea.

Abstract

PubMed is the most extensively used database and search engine in the biomedical and healthcare fields. However, users could experience several difficulties in acquiring their target papers facing massive numbers of search results, especially in their unfamiliar fields. Therefore, we developed a novel user interface for PubMed and conducted three steps of study: step A, a preliminary user survey with 76 medical experts regarding the current usability for the biomedical literature search task at PubMed; step B is implementing EEEvis, a novel interactive visual analytic system for the search task; step C, a randomized user study comparing PubMed and EEEvis. First, we conducted a Google survey of 76 medical experts regarding the unmet needs of PubMed and the user requirements for a novel search interface. According to the data of preliminary Google survey, we implemented a novel interactive visual analytic system for biomedical literature search. This EEEvis provides enhanced literature data analysis functions including (1) an overview of the bibliographic features including publication date, citation count, and impact factors, (2) an overview of the co-authorship network, and (3) interactive sorting, filtering, and highlighting. In the randomized user study of 24 medical experts, the search speed of EEEvis was not inferior to PubMed in the time to reach the first article (median difference 3 sec, 95% CI -2.1 to 8.5, P = 0.535) nor in the search completion time (median difference 8 sec, 95% CI -4.7 to 19.1, P = 0.771). However, 22 participants (91.7%) responded that they are willing to use EEEvis as their first choice for a biomedical literature search task, and 21 participants (87.5%) answered the bibliographic sorting and filtering functionalities of EEEvis as a major advantage. EEEvis could be a supplementary interface for PubMed that can enhance the user experience in the search for biomedical literature.

Copyright: © 2023 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Figures

Fig 1
Fig 1 Study scheme. * The members of the two populations (phase A: n = 76; phase B: n = 24) did not overlap and were completely different. † The E-P group performed the same query in the sequence of EEEvis and PubMed, and the P-E group performed the query in the sequence of PubMed and EEEvis. § This is an example of the E-P group.
Fig 2
Fig 2 System architecture for EEEvis.
Fig 3
Fig 3 Schematic flow for randomized user study. * The E-P group performed the same query in the sequence of EEEvis and PubMed, and the P-E group performed the query in the sequence of PubMed and EEEvis.
Fig 4
Fig 4 Results of preliminary survey. * Multiple choice was possible up to three answers.
Fig 5
Fig 5 EEEvis interfaces. (A) Overview of EEEvis interfaces: (1) Bibliography Filters, (2) Timeline View, (3) Co-authorship Network View, (4) Article List & Detail View, (5) Filter Status Bar. (B) Timeline View. A series of boxplots on an ordinal timeline x-axis. The boxplot is a standard Tukey boxplot (the median, first quartile, and third quartile). The y-axis represents the citation counts with a logarithmic scale. (C) Co-authorship Network View. (1) This view represents the co-authorship network among the most published authors of the targeted subset with a force-directed node-link graph. The numbers of authorships and co-authorships are visually encoded into the node color and link strength. (2) Users can adjust the graph properties with the controls in the optional configuration side panel.
Fig 6
Fig 6 Article list & detail view. The Detail View displays the title, authors, citation counts, publishing source and data, external links to PubMed and PubTator, the abstract, and the PubTator annotations. Users can click on each annotation, which provides external links to the information of the biomedical entity.

그림 출처: PubMed Central 공개 논문 (PMC9910730)

Conflict of interest statement

The authors have declared that no competing interests exist.

그 밖의 정보
출판 형태
Journal Article, Research Support, Non-U.S. Gov't
저널
PloS one · eISSN 1932-6203
전자 출판
2023-02-09
MeSH
Humans · MEDLINE · PubMed · Search Engine* · Databases, Factual
참고문헌
30편