Examining MEDLINE Search Query Reproducibility and Resulting Variation in Search Results C
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Biomedical Text Mining: a Survey of Recent Progress
Chapter 14 BIOMEDICAL TEXT MINING: A SURVEY OF RECENT PROGRESS Matthew S. Simpson Lister Hill National Center for Biomedical Communications United States National Library of Medicine, National Institutes of Health [email protected] Dina Demner-Fushman Lister Hill National Center for Biomedical Communications United States National Library of Medicine, National Institutes of Health [email protected] Abstract The biomedical community makes extensive use of text mining tech- nology. In the past several years, enormous progress has been made in developing tools and methods, and the community has been witness to some exciting developments. Although the state of the community is regularly reviewed, the sheer volume of work related to biomedical text mining and the rapid pace in which progress continues to be made make this a worthwhile, if not necessary, endeavor. This chapter pro- vides a brief overview of the current state of text mining in the biomed- ical domain. Emphasis is placed on the resources and tools available to biomedical researchers and practitioners, as well as the major text mining tasks of interest to the community. These tasks include the recognition of explicit facts from biomedical literature, the discovery of previously unknown or implicit facts, document summarization, and question answering. For each topic, its basic challenges and methods are outlined and recent and influential work is reviewed. Keywords: Biomedical information extraction, named entity recognition, relations, events, summarization, question answering, literature-based discovery C.C. Aggarwal and C.X. Zhai (eds.), Mining Text Data, DOI 10.1007/978-1-4614-3223-4_14, 465 © Springer Science+Business Media, LLC 2012 466 MINING TEXT DATA 1. -
MEDLINE® Comprehensive Biomedical and Health Sciences Information
ValuaBLE CONTENT; HIGhly FOCUSED seaRCH capaBILITIES MEDLINE® COMPREHENSIVE BIOmedical AND health SCIENCES INFORmatiON WHAT YOU’LL FIND WHAT YOU CAN DO • Content from over 5,300 journals in • Keep abreast of vital life sciences information 30 languages, plus a select number of • Uncover relevant results in related fields relevant items from newspapers, magazines, and newsletters • Identify potential collaborators with significant citation records • Over 17 million records from publications worldwide • Discover emerging trends that help you pursue successful research and grant acquisition • Approximately 600,000 records added annually • Target high-impact journals for publishing • Links from MEDLINE records to the valuable your manuscript NCBI protein and DNA sequence databases, and to PubMed Related Articles • Integrate searching, writing, and bibliography creation into one streamlined process • Direct links to your full-text collections • Fully searchable and indexed backfiles to 1950 • Full integration with ISI Web of Knowledge content and capabilities GLOBAL COVERAGE AND SPECIALIZED INDEXING ACCESS THE MOST RECENT INFORMAtion — AS MEDLINE is the U.S. National Library of Medicine® WELL AS BACKFILES TO 1950 (NLM®) premier bibliographic database, covering Discover the most recent information by viewing biomedicine and life sciences topics vital to biomedical MEDLINE In-Process records, recently added records practitioners, educators, and researchers. You’ll find that haven’t yet been fully indexed. And track nearly 60 coverage of the full range of disciplines, such as years of vital backfile data to find the supporting — or medicine, life sciences, behavioral sciences, chemical refuting — data you need. More backfiles give you the sciences, and bioengineering, as well as nursing, power to conduct deeper, more comprehensive searches dentistry, veterinary medicine, the health care system, and track trends through time. -
Scientific Literature Databases and Citation Quality Indicators
International Society for Soil Mechanics and Geotechnical Engineering Société Internationale de Mécanique des Sols et de la Géotechnique SCIENTIFIC LITERATURE DATABASES AND CITATION QUALITY INDICATORS A REPORT FOR THE BOARD OF THE INTERNATIONAL SOCIETY FOR SOIL MECHANICS AND FOUNDATION ENGINEERING Antonio Gens & Pierre Delage August 2015 1. Introduction It is a fact of the current academic environment that the evaluation of individual researchers, funding proposals and even Departments and Universities resort to quantitative indicators related to publication metrics that in turn are based on citation statistics. The availability of rather comprehensive databases incorporating citation data allows the use of those instruments in a generally straightforward manner. Thus, research visibility and academic hiring and promotion have become strongly linked to citation counts. Of course, evaluation and ranking is not the only (or even the main) aim of citation indices; they are also essential for in-depth exploration of an academic discipline or research topic. As Eugene Garfield, the father of citation indexing of academic literature, wrote: “Citations are the formal, explicit linkages between papers that have particular points in common. A citation index is built around these linkages. It lists publications that have been cited and identifies the sources of the citations. Anyone conducting a literature search can find from one to dozens of additional papers on a subject just by knowing one that has been cited. And every paper that is found provides a list of new citations with which to continue the search.” However, in this document attention will be mainly focused on citations as indicators of quality and prestige. -
Information-Seeking Behavior in Complementary and Alternative Medicine (CAM): an Online Survey of Faculty at a Health Sciences Campus*
Information-seeking behavior in complementary and alternative medicine (CAM): an online survey of faculty at a health sciences campus* By David J. Owen, M.L.S., Ph.D. [email protected] Education Coordinator, Basic Sciences Min-Lin E. Fang, M.L.I.S. [email protected] Information Services Librarian Kalmanovitz Library and Center for Knowledge Management University of California, San Francisco San Francisco, California 94143-0840 Background: The amount of reliable information available for complementary and alternative medicine (CAM) is limited, and few authoritative resources are available. Objective: The objective is to investigate the information-seeking behavior of health professionals seeking CAM information. Methods: Data were gathered using a Web-based questionnaire made available to health sciences faculty af®liated with the University of California, San Francisco. Results: The areas of greatest interest were herbal medicine (67%), relaxation exercises (53%), and acupuncture (52%). About half the respondents perceived their CAM searches as being only partially successful. Eighty-two percent rated MEDLINE as a useful resource, 46% personal contacts with colleagues, 46% the Web, 40% journals, and 20% textbooks. Books and databases most frequently cited as useful had information about herbs. The largest group of respondents was in internal medicine (26%), though 15% identi®ed their specialties as psychiatry, psychology, behavioral medicine, or addiction medicine. There was no correlation between specialty and patterns of information- seeking behavior. Sixty-six percent expressed an interest in learning more about CAM resources. Conclusions: Health professionals are frequently unable to locate the CAM information they need, and the majority have little knowledge of existing CAM resources, relying instead on MEDLINE. -
Exploring Biomedical Records Through Text Mining-Driven Complex Data Visualisation
medRxiv preprint doi: https://doi.org/10.1101/2021.03.27.21250248; this version posted March 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Exploring biomedical records through text mining-driven complex data visualisation Joao Pita Costa Luka Stopar Luis Rei Institute Jozef Stefan Institute Jozef Stefan Institute Jozef Stefan Ljubljana, Slovenia Ljubljana, Slovenia Ljubljana, Slovenia [email protected] [email protected] [email protected] Besher Massri Marko Grobelnik Institute Jozef Stefan Institute Jozef Stefan Ljubljana, Slovenia Ljubljana, Slovenia [email protected] [email protected] ABSTRACT learning technologies that have been entering the health domain The recent events in health call for the prioritization of insightful at a slow and cautious pace. The emergencies caused by the recent and meaningful information retrieval from the fastly growing pool pandemics and the need to act fast and accurate were a motivation of biomedical knowledge. This information has its own challenges to fast forward some of the modernization of public health and both in the data itself and in its appropriate representation, enhanc- healthcare information systems. Though, the amount of available ing its usability by health professionals. In this paper we present information and its heterogeneity creates obstacles in its usage in a framework leveraging the MEDLINE dataset and its controlled meaningful ways. vocabulary, the MeSH Headings, to annotate and explore health- related documents. The MEDijs system ingests and automatically annotates text documents, extending their legacy metadata with MeSH Headings. -
1 Application of Text Mining to Biomedical Knowledge Extraction: Analyzing Clinical Narratives and Medical Literature
Amy Neustein, S. Sagar Imambi, Mário Rodrigues, António Teixeira and Liliana Ferreira 1 Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature Abstract: One of the tools that can aid researchers and clinicians in coping with the surfeit of biomedical information is text mining. In this chapter, we explore how text mining is used to perform biomedical knowledge extraction. By describing its main phases, we show how text mining can be used to obtain relevant information from vast online databases of health science literature and patients’ electronic health records. In so doing, we describe the workings of the four phases of biomedical knowledge extraction using text mining (text gathering, text preprocessing, text analysis, and presentation) entailed in retrieval of the sought information with a high accuracy rate. The chapter also includes an in depth analysis of the differences between clinical text found in electronic health records and biomedical text found in online journals, books, and conference papers, as well as a presentation of various text mining tools that have been developed in both university and commercial settings. 1.1 Introduction The corpus of biomedical information is growing very rapidly. New and useful results appear every day in research publications, from journal articles to book chapters to workshop and conference proceedings. Many of these publications are available online through journal citation databases such as Medline – a subset of the PubMed interface that enables access to Medline publications – which is among the largest and most well-known online databases for indexing profes- sional literature. Such databases and their associated search engines contain important research work in the biological and medical domain, including recent findings pertaining to diseases, symptoms, and medications. -
CV Guidelines Regarding Publications
Guidelines for CV: Publications/Creative Activity Index Medicus: http://www2.bg.am.poznan.pl/czasopisma/medicus.php?lang=eng Reference in AHSL: American Medical Association (AMA) Manual of Style, 9th Edition (in reference section behind main desk) *Per Dave Piper, AHSL, underlining of titles is obsolete; italicization is preferred. Below guidelines were established for CoM Annual Report, not CVs in particular, but very similar. Books (scholarly books and monographs, authored or edited, conference proceedings): Author(s)/Editor(s)1; Book title (published conference proceedings go here – include conference title, dates & location); Publisher; Place of publication; Year of publication; Other identifying info Example – book/authors: Alpert JS, Ewy GA; Manual of Cardiovascular Diagnosis and Therapy; Lippincott, Williams & Wilkins; Philadelphia, PA; 2002; 5th edition Example – book/editors: Becker RC, Alpert JS, eds; Cardiovascular Medicine – Practice and Management; Arnold Publishers; London, England; 2001 Chapters (chapters in scholarly books and monographs): Author(s)1; Chapter title; Pages3; Book title; Publisher; Place of publication; Year of publication2; (Other identifying info) Example – Book chapter: Alpert JS, Sabik JF, Cosgrove DM; Mitral valve disease; pp 483-508; In Textbook of Cardiovascular Medicine; Lippincott, Williams & Wilkins; Philadelphia, PA; 2002; Topol, EJ, ed.; 2nd edition Example – Monograph: Alpert JS; Recent advances in the management of patients with acute myocardial infarction; 76:81-172; Monograph published in -
Conducting a Successful Literature Search: a Researcher's Guide To
Conducting a successful literature search: A researcher’s guide to tools, terms and techniques Finding high-quality information can be a challenge. Sometimes you need help, but you aren’t able to speak directly with an expert. Reference these cards when you need quick support—think of this as a Librarian in your back pocket! 1. Keywords, Operators & Filters 2. Search Tools 3. Types of Literature 4. Evaluate Information 5. Organize Research Conducting a successful literature search: A researcher’s guide to tools, terms and techniques 1. Keywords, operators and filters Brainstorm Expand Use Refine keywords your keywords boolean operators your search results These are the main ideas of your Look at the subject headings of the Insert AND, OR, and NOT into your Filters in the database allow you to research question/topic sentence. materials you find and use those search to broaden or narrow it. narrow a search by year, content type, etc. terms as applicable. For example: Or look up your keywords in a PTSD OR Post Traumatic Stress subject-specific database thesaurus Disorder AND soldiers NOT Navy. to find predefined terms (called “controlled vocabulary”). At the library: Consult a liaison librarian or subject specialist. 1 Conducting a successful literature search: A researcher’s guide to tools, terms and techniques 2. Search tools Select the best tools: Abstract and Full-text database Search box on Library catalog Web search engine citation database When you’re ready to dive library homepage When looking for items housed When looking for popular and Short descriptions (abstracts) deeper into research, seek out Discover your library’s full physically in the library (as well widely-available content, web of research content so you searchable, multidisciplinary catalog to view a wide array of as some electronic items). -
Biochemical and Biophysical Research Communications
BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS AUTHOR INFORMATION PACK TABLE OF CONTENTS XXX . • Description p.1 • Audience p.1 • Impact Factor p.2 • Abstracting and Indexing p.2 • Editorial Board p.2 • Guide for Authors p.4 ISSN: 0006-291X DESCRIPTION . BBRC -- the fastest submission-to-online journal! From Submission to Online in Less Than 3 Weeks! Biochemical and Biophysical Research Communications is the premier international journal devoted to the very rapid dissemination of timely and significant experimental results in diverse fields of biological research. The development of the "Breakthroughs and Views" section brings the minireview format to the journal, and issues often contain collections of special interest manuscripts. BBRC is published weekly (52 issues/year). Research Areas now include: • Biochemistry • Bioinformatics • Biophysics • Cancer Research • Cell Biology • Developmental Biology • Immunology • Molecular Biology • Neurobiology • Plant Biology • Proteomics Benefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services. Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our Support Center AUDIENCE . Biochemists, bioinformaticians, biophysicists, immunologists, cancer researchers, stem cell scientists and neurobiologists. AUTHOR INFORMATION PACK 24 Sep 2021 www.elsevier.com/locate/ybbrc -
Document Clustering of Medline Abstracts for Concept Discovery in Molecular Biology
Pacific Symposium on Biocomputing 6:384-395 (2001) TEXTQUEST: DOCUMENT CLUSTERING OF MEDLINE ABSTRACTS FOR CONCEPT DISCOVERY IN MOLECULAR BIOLOGY I. ILIOPOULOS, A. J. ENRIGHT, C. A. OUZOUNIS Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK {ioannis, anton, christos}@ebi.ac.uk We present an algorithm for large-scale document clustering of biological text, obtained from Medline abstracts. The algorithm is based on statistical treatment of terms, stemming, the idea of a ‘go-list’, unsupervised machine learning and graph layout optimization. The method is flexible and robust, controlled by a small number of parameter values. Experiments show that the resulting document clusters are meaningful as assessed by cluster-specific terms. Despite the statistical nature of the approach, with minimal semantic analysis, the terms provide a shallow description of the document corpus and support concept discovery. 1. Introduction The vast accumulation of electronically available textual information has raised new challenges for information retrieval technology. The problem of content analysis was first introduced in the late 60’s [1]. Since then, a number of approaches have emerged in order to exploit free-text information from a variety of sources [2, 3]. In the fields of Biology and Medicine, abstracts are collected and maintained in Medline, a project supported by the U.S. National Library of Medicine (NLM)1. Medline constitutes a valuable resource that allows scientists to retrieve articles of interest, based on keyword searches. This query-based information retrieval is extremely useful but it only allows a limited exploitation of the knowledge available in biological abstracts. -
Medline Vs Embase
MEDLINE VS EMBASE Prior to starting a search, it is essential to choose the most appropriate database. The principal sources used to search topics related to biomedicine and health Medline and Embase. What are their characteristics? Medline Embase1 Focus Biomedicine and health Broad biomedical scope with in-depth coverage of drugs and pharmacology Produced by US National Library of Medicine Elsevier Access Available free of charge via PubMed OR Through institutional subscription via Ovid. Through institutional subscription via Ovid. Available via MUHC libraries portal Available via MUHC libraries portal muhclibraries.ca > Quick Links section muhclibraries.ca > Quick Links section Content Journal articles, mostly from peer-reviewed Journal articles, mostly from peer-reviewed journals journals + conference abstracts since 2009 # of records Over 30 million Over 32 million, including all Medline records Including 26 million indexed for Medline (i.e. 6 million are unique to Embase) # of journals 5,244 indexed for Medline Almost 8,500, including Medline unique journals (i.e. more than 2900 journals unique to Embase) 1 https://www.elsevier.com/solutions/embase-biomedical-research/embase-coverage-and-content The searcher should also be familiar with differences in the establishment of subject headings in each database. These differences impact the way we search and the results. Medline MeSH Embase Emtree2 # of terms 29,351 Over 75,000 (of which more than 32,000 are drugs and chemicals) # of 83 78 (of which 64 are drug subheadings subheadings including routes of drug administration) Synonyms 681,5053 320,000 Thesaurus Annually 3 times per year update Drug terms are included when they become Drug terms are included early in the drug established development process # of terms per 10-20 3-4 major terms, and up to 50 minor terms.4 article Medline-derived articles are not indexed with Emtree terms. -
Health Sciences Librarians' Engagement in Open Science: A
Health sciences librarians’ engagement in open science: A scoping review Project Team: Dean Giustini Kevin Read Ariel Deardorff Lisa Federer Melissa Rethlefsen MLA Annual Conference, May 2021 Health sciences librarians’ engagement in open science: A scoping review Objectives: To identify health sciences librarians’ (HSLs) engagement in open science (OS) through the delivery of library services, support, and programs for researchers. Methods: We performed a scoping review guided by Arksey and O’Malley’s (2005) framework and Joanna Briggs’ Manual for scoping reviews. Our search methods consisted of searching five databases (MEDLINE, Embase, CINAHL, LISTA, and Web of Science Core Collection), tracking citations, contacting experts, and targeted web searching. We used Zotero to manage citations, and Covidence for screening. To determine study eligibility, we applied predetermined inclusion and exclusion criteria, achieving consensus among reviewers when there was disagreement. Finally, we extracted data in duplicate and performed qualitative analysis to map key themes. Results & discussion HSL Open Science Scoping Review / Giustini, Read, Deardorff, Federer, Rethlefsen / MLA ‘21 Health sciences librarians’ engagement in open science: A scoping review Results: We identified 54 included studies (after reviewing 8173 citations / 319 full text studies). Research methods included descriptive or narrative approaches (76%), surveys, questionnaires and interviews (15%) or mixed methods (9%). Using FOSTER’s Open Science Taxonomy, we labeled studies using six themes: open access (54%), open data (43%), open science (24%), and open education, open source and citizen science (17%). Key drivers in OS were scientific integrity and transparency, openness as a guiding principle, and funder mandates making research openly-accessible. HSLs engaged in OS advocacy and most examples came from academic institutions.