http://www.bibsonomy.org/burst/tag/PubMedBibSonomy BuRST Feed for /tag/PubMed2008-11-19T05:47:40+01:00PubMed related article algorithmhttp://www.bibsonomy.org/bibtex/2c86ccd0ff11afe61f66455e24966796f/tbergtberg2008-09-18T23:39:57+02:00pubmed imported related <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Lin">Lin</a> und W.J. <a href="http://www.bibsonomy.org/author/Wilbur">Wilbur</a> </span><em>BMC Bioinformatics</em><em>8(1):423</em>(<em>2007</em>)Thu Sep 18 23:39:57 CEST 2008BMC Bioinformatics1423{PubMed related articles: A probabilistic topic-based model for content similarity}82007pubmed imported related Background: We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance—but rather our focus is “relatedness”, the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH R in MEDLINE R . Results: The pmra retrieval model was compared against bm25, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of pmra over bm25 in terms of precision. Conclusions: Our experiments suggest that the pmra model provides an effective ranking algorithm for related article search.PubMed related article algorithmhttp://www.bibsonomy.org/bibtex/29f2c7023e2f4ed00ed5680eff53c3cd8/tbergtberg2008-09-18T23:37:43+02:00pubmed imported related <span style="color:#555555;">W.J. <a href="http://www.bibsonomy.org/author/Wilbur">Wilbur</a> </span><em>Medical Informatics: Knowledge Management and Data Mining in Biomedicine</em>(<em>2005</em>)Thu Sep 18 23:37:43 CEST 2008Medical Informatics: Knowledge Management and Data Mining in Biomedicine277--297{Modeling Text Retrieval in Biomedicine}2005pubmed imported related Given the amount of literature relevant to many of the areas of biomedicine, researchers are forced to use methods other than simply reading all the literature on a topic. Necessarily one must fall back on some kind of search engine. While the Google PageRank algorithm works well for finding popular web sites, it seems clear one must take a different approach in searching for information needed at the cutting edge of research. Information which is key to solving a particular problem may never have been looked at by many people in the past, yet it may be crucial to present progress. What has worked well to meet this need is to rank documents by their probable relevance to a piece of text describing the information need (a query). Here we will describe a general model for how this is done and how this model has been realized in both the vector and language modeling approaches to document retrieval. This approach is quite broad and applicable to much more than biomedicine. We will also present three example document retrieval systems that are designed to take advantage of specific information resources in biomedicine in an attempt to improve on the general model. Current challenges and future prospects are also discussed.Leitlinienwartunghttp://www.bibsonomy.org/bibtex/2ef279d6f1a4dede5f859c7a41c8190d9/magermager2008-08-12T10:26:05+02:00Intelligence; MEDLINE; Medicine; PubMed and Information Storage Evidence-Based Algorithms; Bibliometrics; Artificial Internet; Retrieval/methods; <span style="color:#555555;">E. V. <a href="http://www.bibsonomy.org/author/Bernstam">Bernstam</a> und J. R. <a href="http://www.bibsonomy.org/author/Herskovic">Herskovic</a> und Y. <a href="http://www.bibsonomy.org/author/Aphinyanaphongs">Aphinyanaphongs</a> und C. F. <a href="http://www.bibsonomy.org/author/Aliferis">Aliferis</a> und M. G. <a href="http://www.bibsonomy.org/author/Sriram">Sriram</a> und W. R. <a href="http://www.bibsonomy.org/author/Hersh">Hersh</a> </span><em>Journal of the American Medical Informatics Association : JAMIA</em><em>13(1):96-105</em><em>Jan-Feb2006. </em><em>LR: 20061115; PUBM: Print-Electronic; GR: 5 K22 LM008306/LM/NLM; DEP: 20051012; JID: 9430800; 2005/10/12 [aheadofprint]; 2005/10/14 [aheadofprint]; ppublis<span class="info">...<span>LR: 20061115; PUBM: Print-Electronic; GR: 5 K22 LM008306/LM/NLM; DEP: 20051012; JID: 9430800; 2005/10/12 [aheadofprint]; 2005/10/14 [aheadofprint]; ppublish</span></span> . </em>Tue Aug 12 10:26:05 CEST 2008Journal of the American Medical Informatics Association : JAMIAJan-FebLR: 20061115; PUBM: Print-Electronic; GR: 5 K22 LM008306/LM/NLM; DEP: 20051012; JID: 9430800; 2005/10/12 [aheadofprint]; 2005/10/14 [aheadofprint]; ppublish196-105Using citation data to improve retrieval from MEDLINE132006Intelligence; MEDLINE; Medicine; PubMed and Information Storage Evidence-Based Algorithms; Bibliometrics; Artificial Internet; Retrieval/methods; OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.http://www.bibsonomy.org/bibtex/24baeafb979411f3c1dd1b1d50937967e/willwadewillwade2007-02-16T15:24:54+01:00medline critique pubmed research <span style="color:#555555;">T. <a href="http://www.bibsonomy.org/author/Greenhalgh">Greenhalgh</a> </span><em>BMJ</em><em>315(7101):180--183</em><em>July1997. </em>Fri Feb 16 15:24:54 CET 2007Department of Primary Care and Population Sciences, University College London Medical School/Royal Free Hospital School of Medicine, Whittington Hospital, London. p.greenhalgh@ucl.ac.ukBMJJuly7101180--183How to read a paper. The Medline database.3151997medline critique pubmed research