A Day in the Life of PubMed: Analysis of a Typical Day's Query Log

Herskovic, Jorge R.; Tanaka, Len; Hersh, William R.; Bernstam, Elmer V.

doi:10.1197/jamia.m2191

Cited by 140 publications

(129 citation statements)

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“…Très peu utilisé par les chercheurs [15], l'usage des descripteurs MeSH s'avère pourtant être un outil très pratique pour apporter beaucoup plus de précision dans les recherches avec PubMed.…”

Section: Utiliser Le Thésaurus Medical Subject Heading (Mesh) Pour Réunclassified

Comment utiliserPubMedpour les recherches bibliographiques en éducation thérapeutique du patient

Andrade

2014

Educ Ther Patient/Ther Patient Educ

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Résumé -Introduction : L'éducation thérapeutique du patient (ETP) est un champ d'étude apportant un nombre croissant de publications scientifiques, principalement dans des revues médicales. Dans ce domaine, la référence des moteurs de recherche est PubMed, interface gratuite développée par la bibliothèque de médecine américaine (NLM) pour interroger sa base de données MEDLINE. Cet article aborde la recherche bibliographique en ETP. Comment interroger PubMed ? Quels sont les mots clés disponibles ? Discussion : Certaines équations de recherche utilisables par le chercheur sont décrites ici. Les deux problématiques habituellement rencontrées dans les recherches bibliographiques sont le « bruit » (trop de résultats et peu de pertinence) et le « silence » (manque de résultats)

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Section: Utiliser Le Thésaurus Medical Subject Heading (Mesh) Pour Réunclassified

Comment utiliserPubMedpour les recherches bibliographiques en éducation thérapeutique du patient

Andrade

2014

Educ Ther Patient/Ther Patient Educ

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“…They also have been used to analyze image search behavior [9,10]. Analysis of MedLine search behavior in the medical literature was conducted based on log files [11,12]. Closest to the presented work are the analyses of Tsikrika et al [7] and Rubin et al [13] that both used ARRS GoldMiner log files, but a much smaller set of queries (25,000 and 30,000, respectively, so around 10 % of the data used in this text).…”

Section: Introductionmentioning

confidence: 99%

Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results

et al. 2015

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Log files of information retrieval systems that record user behavior have been used to improve the outcomes of retrieval systems, understand user behavior, and predict events. In this article, a log file of the ARRS GoldMiner search engine containing 222,005 consecutive queries is analyzed. Time stamps are available for each query, as well as masked IP addresses, which enables to identify queries from the same person. This article describes the ways in which physicians (or Internet searchers interested in medical images) search and proposes potential improvements by suggesting query modifications. For example, many queries contain only few terms and therefore are not specific; others contain spelling mistakes or non-medical terms that likely lead to poor or empty results. One of the goals of this report is to predict the number of results a query will have since such a model allows search engines to automatically propose query modifications in order to avoid result lists that are empty or too large. This prediction is made based on characteristics of the query terms themselves. Prediction of empty results has an accuracy above 88 %, and thus can be used to automatically modify the query to avoid empty result sets for a user. The semantic analysis and data of reformulations done by users in the past can aid the development of better search systems, particularly to improve results for novice users. Therefore, this paper gives important ideas to better understand how people search and how to use this knowledge to improve the performance of specialized medical search engines.

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“…While we recognize that this concern is valid, we still believe that a system has to provide the possibility of processing boolean queries. In fact, a PUBMED query log [23] with approximately 3 million queries shows that 36.5% of the queries were boolean queries. Although many of these queries might be computer generated (e.g.…”

Section: Applying Boolean Queries With Bm25mentioning

confidence: 99%

“…We tried to run our tests using 4.5 Millions documents, a subset of MEDLINE entries on an Apple iMac machine with 2.93GHz Intel i7 CPU (quad core) and 8GB RAM. Using the aforementioned queries from the Pubmed one-day query log [23] (see Section 3.4), the search speeds were measured as follows. First, focusing on the speed as function of the document frequency, which varied from 1 document to 1.3 Millions documents and with one term in each query, the execution speed was constantly below 100 ms.…”

Section: Fig 6 Graphs For Top-k Precision Valuesmentioning

confidence: 99%

Supporting BioMedical Information Retrieval: The BioTracer Approach

Ramampiaro

2011

Transactions on Large-Scale Data- And Knowledge-Centered Systems IV

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Abstract. The large amount and diversity of available biomedical information has put a high demand on existing search systems. Such a tool should be able to not only retrieve the sought information, but also filter out irrelevant documents, while giving the relevant ones the highest ranking. Focusing on biomedical information, this work investigates how to improve the ability for a system to find and rank relevant documents. To achieve this goal, we apply a series of information retrieval techniques to search in biomedical information and combine them in an optimal manner. These techniques include extending and using well-established information retrieval (IR) similarity models such as the Vector Space Model (VSM) and BM25 and their underlying scoring schemes. The techniques also allow users to affect the ranking according to their view of relevance. The techniques have been implemented and tested in a proofof-concept prototype called BioTracer, which extends a Java-based open source search engine library. The results from our experiments using the TREC 2004 Genomic Track collection are promising. Our investigation have also revealed that involving the user in the search process will indeed have positive effects on the ranking of search results, and that the approaches used in BioTracer can be used to meet the user's information needs.Keywords: Biomedical Information Retrieval, Evaluation, BioTracer. Background and MotivationThe continuous increase in the amount of available biomedical information has resulted in a higher demand on biomedical information retrieval (IR) systems. While their use has helped researchers in the field to stay updated on recent literature, many of the existing search systems tend to be either too restrictive (returning results with a low recall) or too broad (finding results with a This article is a revised and an extended version of the ITBAM 2010 paper [1].

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A Day in the Life of PubMed: Analysis of a Typical Day's Query Log

Abstract: PubMed's usage profile should be considered when educating users, building user interfaces, and developing future biomedical information retrieval systems.

Cited by 140 publications

References 11 publications

Comment utiliserPubMedpour les recherches bibliographiques en éducation thérapeutique du patient

Comment utiliserPubMedpour les recherches bibliographiques en éducation thérapeutique du patient

Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results

Supporting BioMedical Information Retrieval: The BioTracer Approach

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