VizMaps: A Bayesian Topic Modeling Based PubMed Search Interface

A common challenge that users of academic databases face is making sense of their query outputs for knowledge discovery. This is exacerbated by the size and growth of modern databases. PubMed, a central index of biomedical literature, contains over 25 million citations, and can output search results containing hundreds of thousands of citations. Under these conditions, efficient knowledge discovery requires a different data structure than a chronological list of articles. It requires a method of conveying what the important ideas are, where they are located, and how they are connected; a method of allowing users to see the underlying topical structure of their search. This paper presents VizMaps, a PubMed search interface that addresses some of these problems. Given search terms, our main backend pipeline extracts relevant words from the title and abstract, and clusters them into discovered topics using Bayesian topic models, in particular the Latent Dirichlet Allocation (LDA). It then outputs a visual, navigable map of the query results.

Evidence-Based Practice in PubMed: Are Shared Filters Useful to Health Sciences Academic Users?

Objectives In April 2010, the Université de Montréal’s Health Sciences Library has implemented shared filters in its institutional PubMed account. Most of these filters are designed to highlight resources for evidence-based practice, such as Clinical Queries, Systematic Reviews and Evidence-based Synopsis. We now want to measure how those filters are perceived and used by our users. Methods For one month, data was gathered through an online questionnaire proposed to users of Université de Montréal’s PubMed account. A print version was also distributed to participants in information literacy workshops given by the health sciences librarians. Respondents were restricted to users affiliated to Université de Montréal’s faculties of Medicine, Dentistry, Veterinary Sciences, Nursing and Pharmacy. Basic user information such as year/program of study or department affiliation was also collected. The questionnaire allowed users to identify the filters they use, assess the relevance of filters, and also suggest new ones. Results Survey results showed that the shared filters of Université de Montreal’s PubMed account were found useful by the majority of respondents. Filters allowing rapid access to secondary resources ranked among the most relevant (Reviews, Systematic Reviews, Cochrane Database of Systematic Reviews, Practice Guidelines and Clinical Evidence). For Clinical Study Queries, Randomized Controlled Trial (Therapy/Narrow) was considered the most useful. Some new shared filters have been suggested by respondents. Finally, 18% of the respondents indicated that they did not quite understand the relevance of filters. Conclusion Based on the survey results, shared filters considered most useful will be kept, some will be enhanced and others removed so that suggested ones could be added. The fact that some respondents did not understand well the relevance of filters could potentially be addressed through our PubMed workshops, online library guides or by renaming some filters in a more meaningful way.

Einführung in die systematische Literaturrecherche mit den Datenbanken "PsycINFO", "Pubmed" und "PEP – Psychoanalytic Electronic Publishing" sowie in das Literaturverwaltungsprogramm "Citavi"

Bewegt man sich als Studentin oder Student in psychologischen, medizinischen oder pädagogischen Studiengängen, ist häufig eine systematische Literaturrecherche unerlässlich, um sich über den neuesten Forschungsstand in einem bestimmten Themenfeld zu informieren oder sich in ein neues Thema beispielsweise für die Abschlussarbeit einzuarbeiten. Literaturrecherchen sind zentraler Bestandteil jeden wissenschaftlichen Arbeitens. Die recherchierten Literaturangaben und Quellen bilden die Bausteine, auf denen die Darstellung des Wissens in Hausarbeiten, Magister-, Diplomarbeiten und später Doktorarbeiten basiert. In Form von Quellenangaben, z.B. durch indirekte oder direkte Zitate oder Paraphrasierungen werden Bezugnahmen auf die bereits vorhandene Literatur transparent gemacht. Häufig genügt ein Blick auf eine Literaturliste um zu erkennen, wie Literatur gesucht und zusammengestellt wurde. Wissenschaftliches Arbeiten unterscheidet sich von künstlerischem Arbeiten durch seine Systematik und intersubjektive Nachvollziehbarkeit. Diese Systematik sollte bereits bei der Literaturrecherche beginnen und sich am Ende der Arbeit in der Literaturliste widerspiegeln. Auch wenn in einer Magister-, Diplom- und noch weniger in einer Hausarbeit die gesamte, gefundene Literatur verwendet wird, sondern nur eine sehr kleine Auswahl in die Arbeit einfließt, ist es anstrebenswert, sich über die möglichen Suchstrategien im Einzelnen klar zu werden und sich systematisch durch den Berg von Literatur(einträgen) nach einer Recherche zu einer einschlägigen und begründeten Auswahl vorzuarbeiten. Hier stellen die schnell wachsenden Wissensbestände eine besondere Herausforderung an Studierende und Wissenschaftler. Die Recherche am „Zettelkasten‟ in der Bibliothek ist durch die Online-Recherche ersetzt worden. Wissenschaftliche Literatur wird heute in erster Linie digital gesucht, gefunden und verwaltet. Für die Literatursuche steht eine Vielfalt an Suchmaschinen zur Verfügung. Doch welche ist die richtige? Und wie suche ich systematisch? Wie dokumentiere ich meine Suche? Wie komme ich an die Literatur und wie verwalte ich die Literatur? Diese und weitere Fragen haben auch wir uns gestellt und für alle Studierenden der Fächer Psychologie, Psychoanalyse, Medizin und Pädagogik diese Handreichung geschrieben. Sie will eine Hilfe bei der konkreten Umsetzung einer Literaturrecherche sein.

How Twitter is studied in the medical professions: a classification of Twitter papers indexed in PubMed

Background: Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. // Objective: This paper aims to identify published work relating to Twitter indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Limiting the study to papers indexed by PubMed ensures the work provides a reproducible benchmark. // Methods: Papers, indexed by PubMed, on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain and aspect. // Results: As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focussed on Twitter (the others referring to it tangentially). The early Twitter focussed papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. // Conclusions: This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used five dimensions to categorise published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research relating to Twitter in the area of medicine and beyond, can position and ground their work.

Subsídios para construção de estratégias de busca para revisões sistemáticas na base da dados Medline via Pubmed

Pós-graduação em Bases Gerais da Cirurgia - FMB

Testing search strategies for systematic reviews in the Medline literature database through PubMed

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A day in the life of PubMed: analysis of a typical day's query log.

OBJECTIVE: To characterize PubMed usage over a typical day and compare it to previous studies of user behavior on Web search engines. DESIGN: We performed a lexical and semantic analysis of 2,689,166 queries issued on PubMed over 24 consecutive hours on a typical day. MEASUREMENTS: We measured the number of queries, number of distinct users, queries per user, terms per query, common terms, Boolean operator use, common phrases, result set size, MeSH categories, used semantic measurements to group queries into sessions, and studied the addition and removal of terms from consecutive queries to gauge search strategies. RESULTS: The size of the result sets from a sample of queries showed a bimodal distribution, with peaks at approximately 3 and 100 results, suggesting that a large group of queries was tightly focused and another was broad. Like Web search engine sessions, most PubMed sessions consisted of a single query. However, PubMed queries contained more terms. CONCLUSION: PubMed's usage profile should be considered when educating users, building user interfaces, and developing future biomedical information retrieval systems.