Medical Subject Headings Headings Research Articles

MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.

Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management.

Summary Objectives : To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods : A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results : While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions : The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM.

Management Lessons for High-Functioning Primary Care Teams

INTRODUCTIONT he need for accessible, efficient, cost-effective, and quality is a recurring theme in the literature. The 15-minute physician visit model fails to provide acute, chronic, and preventive and does not enable providers to build lasting relationships with patients or manage multiple diagnoses in line with evidence-based recommendations (Bodenheimer & Laing, 2007). These problems have caused a resurgence of interest in team-based among key stakeholders, including government officials, providers, policy analysts, and researchers (Institute of Medicine, 2011; Iglehart, 2009). The rise of integrated models, such as accountable organizations and patient-centered medical homes (PCMI Is), the critical shortage of physicians, the increasing number of patients covered by insurance since passage of the Affordable Care Act, the prioritization of training, and the call for payment reform all suggest a solution that includes implementation of team-based (Iglehart, 2009). By incorporating team-based frameworks and lessons from stakeholders can create innovative and holistic solutions for that enable to achieve goals (Hackman, 1990). The purpose of this literature review is to examine the extensive management literature on this topic, as well as current trends and recommendations in the healthcare literature. T his review focuses on applying this knowledge to and considers the preliminary research conducted on teams.METHODSStudy DesignWe conducted an extensive, semistructured review of management and healthcare literature to identify research on teams. We identified articles in the Business Source Complete (Premier, Elite, and Main Edition), Academic Search Complete (Premier, Elite, and Main Edition), Coogle Scholar, and PubMed databases, and we also identified relevant reports and white papers on prominent healthcare websites, including-but not limited to-The Commonwealth Fund, The Robert Wood Johnson Foundation, and T he Johns I Jopkins Primary Care Consortium. In addition, we examined cited works and references to discover additional publications. The following key search terms, as well as stems, variants, and synonyms, were used in combination across the databases: management, highfunctioning, high-performing, and primary Additionally, the MeSH (Medical Subject Headings) headings primary health care and patient were used in PubMed to focus results. We queried databases and collected articles dating from 1979 to 2015.Inclusion Criteria and Articles IdentifiedAll articles identified in the preliminary searches were selected because of their interdisciplinarity and potential applicability to healthcare. Management searches yielded results from the fields of general service operations crisis organizational behavior, and strategy. We excluded articles from the field of information services because we determined that their technologyfocused team structure was too narrow and did not directly apply to healthcare. Our search for teams and teamwork included, but was not limited to, literature detailing members of a team and their behavior, planning, and execution of teamwork, situations requiring teams, and benefits and challenges of teams. We also included seminal literature on interprofessional and cross-functional teams. For the terms functioning and high performing, we included literature pertaining to the distinction between successful and unsuccessful teams. When searching for the term we included only literature relevant to the field in general; with the exception of care, subspecialties were excluded. Within care, we included existing models and evidence of team-based for adults from internal medicine, family medicine, pediatrics, and geriatrics; literature focusing on mental health was excluded because of its lack of integration with care. …

MeSHSim: An R/Bioconductor package for measuring semantic similarity over MeSH headings and MEDLINE documents.

Currently, all MEDLINE documents are indexed by medical subject headings (MeSH). Computing semantic similarity between two MeSH headings as well as two documents has become very important for many biomedical text mining applications. We develop an R package, MeSHSim, which can compute nine similarity measures between MeSH nodes, by which similarity between MeSH headings as well as MEDLINE documents can be easily computed. Also, MeSHSim supports querying hierarchy information of a MeSH heading and retrieving MeSH headings of a query document, and can be easily integrated into pipelines for any biomedical text analysis tasks. MeSHSim is released under general public license (GPL), and available through Bioconductor and from Github at https://github.com/JingZhou2015/MeSHSim.

Creating Provincial and Territorial Search Filters to Retrieve Studies Related to Canadian Indigenous Peoples from Ovid MEDLINE

Introduction: Performing systematic review searches related to Canadian Indigenous peoples (First Nations, Inuit, and Métis), particularly in areas of public health, is difficult because Medical Subject Headings (MeSH) terms for both Indigenous peoples and geography do not retrieve all relevant articles in Ovid MEDLINE. Text–word searching for Canadian Indigenous peoples presents challenges in the varieties of names, spellings, and languages. A series of Canadian Indigenous peoples filters were designed to retrieve larger numbers of relevant articles. Objectives: The objectives of this work were (i) to create first-generation search filters that retrieve studies from the Ovid MEDLINE database related to Canadian Indigenous peoples, (ii) to determine whether or not the filters retrieve more records than do searches using the MeSH headings alone, and (iii) to determine how many of the additional records are relevant. Methods: Key terms describing both Canadian Indigenous peoples and Canadian geography were identified using government, historical, and ethnographic publications. Name lists included current and historical names in multiple languages, as well as local and settlement names, and names of linguistic groups. Filters, employing both text–word and MeSH terms were created for each province and territory, excluding Prince Edward Island. Search results were reviewed for false recalls related to terms with multiple meanings and groups of people whose lands straddle provincial and territorial borders. Revised searches were refined with additional terminology that implies the presence of Indigenous peoples. Duplicate records were removed from both the MeSH searches and the filter searches. Results from the MeSH searches were then removed from the results of the filter searches. The remaining results were analyzed for relevance. Results: Twelve Ovid MEDLINE filters were created and the challenges involved in creating them were documented. The filters increased recall by 58 articles, 464% over MeSH searches alone. Of the additional articles retrieved, 28 (100%) met the criteria for relevance. Discussion: The lists of challenges identified in the filter creation will assist other searchers in developing similar filters. The filters allow searchers to retrieve substantially more articles than is currently possible with the MeSH terms alone.

Creating Provincial and Territorial Search Filters to Retrieve Studies Related to Canadian Indigenous Peoples from Ovid MEDLINE

Introduction: Performing systematic review searches related to Canadian Indigenous peoples (First Nations, Inuit, and Métis), particularly in areas of public health, is difficult because Medical Subject Headings (MeSH) terms for both Indigenous peoples and geography do not retrieve all relevant articles in Ovid MEDLINE. Text–word searching for Canadian Indigenous peoples presents challenges in the varieties of names, spellings, and languages. A series of Canadian Indigenous peoples filters were designed to retrieve larger numbers of relevant articles. Objectives: The objectives of this work were (i) to create first-generation search filters that retrieve studies from the Ovid MEDLINE database related to Canadian Indigenous peoples, (ii) to determine whether or not the filters retrieve more records than do searches using the MeSH headings alone, and (iii) to determine how many of the additional records are relevant. Methods: Key terms describing both Canadian Indigenous peoples and Canadian geography were identified using government, historical, and ethnographic publications. Name lists included current and historical names in multiple languages, as well as local and settlement names, and names of linguistic groups. Filters, employing both text–word and MeSH terms were created for each province and territory, excluding Prince Edward Island. Search results were reviewed for false recalls related to terms with multiple meanings and groups of people whose lands straddle provincial and territorial borders. Revised searches were refined with additional terminology that implies the presence of Indigenous peoples. Duplicate records were removed from both the MeSH searches and the filter searches. Results from the MeSH searches were then removed from the results of the filter searches. The remaining results were analyzed for relevance. Results: Twelve Ovid MEDLINE filters were created and the challenges involved in creating them were documented. The filters increased recall by 58 articles, 464% over MeSH searches alone. Of the additional articles retrieved, 28 (100%) met the criteria for relevance. Discussion: The lists of challenges identified in the filter creation will assist other searchers in developing similar filters. The filters allow searchers to retrieve substantially more articles than is currently possible with the MeSH terms alone.

MeSH indexing based on automatically generated summaries.

BackgroundMEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results.ResultsWe have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision.ConclusionsOur results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading.

A One-Size-Fits-All Indexing Method Does Not Exist: Automatic Selection Based on Meta-Learning

We present a methodology that automatically selects indexing algorithms for each heading in Medical Subject Headings (MeSH), National Library of Medicine's vocabulary for indexing MEDLINE. While manually comparing indexing methods is manageable with a limited number of MeSH headings, a large number of them make automation of this selection desirable. Results show that this process can be automated, based on previously indexed MEDLINE citations. We find that AdaBoostM1 is better suited to index a group of MeSH hedings named Check Tags, and helps improve the micro F-measure from 0.5385 to 0.7157, and the macro F-measure from 0.4123 to 0.5387 (both p < 0.01).

The Availability of MeSH in Vendor-Supplied Cataloguing Records, as Seen Through the Catalogue of a Canadian Academic Health Library

This study examines the prevalence of medical subject headings in vendor-supplied cataloguing records for publications contained within aggregated databases or publisher collections. In the first phase, the catalogue of one Canadian academic medical library was examined to determine the extent to which medical subject headings (MeSH) are available in the vendor-supplied records. In the second phase, these results are compared to the catalogues of other Canadian academic medical libraries in order to reach a generalization regarding the availability of MeSH headings for electronic resources. MeSH was more widespread in records for electronic journals but was noticeably lacking in records for electronic monographs, and for Canadian publications. There is no standard for ensuring MeSH is assigned to monograph records for health titles and there is no library in Canada with responsibility for ensuring that Canadian health publications receive Medical Subject Headings. It is incumbent upon libraries using MeSH to ensure that vendors are aware of this need when purchasing record sets.

Data quality improvement in general practice

The importance of routine data generated by GPs has grown extensively in the last decade. These data have found many applications other than patient care. More attention has therefore been given to the issue of data quality. Several systematic reviews have detected ample space for improvement of data quality. A new review was conducted in order to find out which methods of improvement are effective. The Medline database was searched using an iteratively composed set of terms and MeSH (Medical Subject Headings) headings. Only papers that focused on explicit attempts at improving data quality of medical records in general practice were included. Twelve studies met the inclusion criteria. No study used patient-based comparison of records with external sources as the method to assess data quality improvement. Ten studies used internal indicators or markers of data quality instead. Attempts at data quality improvement often involve some sort of individualized feedback, and nearly all attempts seem to have some positive effect. Only one of the included studies fulfilled the basic methodological requirements of an intervention study. The most recent studies used a simple before-after design. No intervention to improve data quality has been put to a rigorous enough test. We still lack empirical knowledge as to how improvement can be brought about.

Analysis of current nuclear cardiology literature in MEDLINE database: a study of gated SPECT imaging using PubMed

Background Easy access to relevant clinical information is necessary for physicians to make the best decisions for patient management. The increasing amount of information available has made it difficult for physicians to retrieve up-to-date information efficiently. We sought to determine the accessibility and accuracy of indexing in the nuclear cardiology literature by conducting sample searches in the MEDLINE database on the topic of gated single photon emission computed tomography (SPECT) imaging. Methods and results The MEDLINE database was initially searched by use of both a primary and a comprehensive search strategy on PubMed for publications in English from 1994 to 2000. A total of 260 papers were retrieved from the primary search and 306 additional papers from the comprehensive search. Only 204 of the 566 citations from the combined electronic searches were truly relevant to gated SPECT. The resulting specificity index (precision) was 36%. A hand search was conducted in 11 top journals from 1994 to 2000. It yielded 81 additional citations that were missed by the PubMed search. The sensitivity index (recall) was calculated for all 11 journals. The Journal of Nuclear Cardiology had the highest rate of publication but the lowest rate of recall (44%). The clinical nuclear cardiology terminology and classifications were compared with the available Medical Subject Headings (MeSH) and MeSH Trees used for indexing in MEDLINE. Conclusions There are 6 nuclear cardiology techniques, including gated SPECT and myocardial perfusion imaging, that are not specifically indexed in the current MEDLINE database. The lack of specific MeSH headings and indexing structure results in low recall and precision for retrieval of nuclear cardiology literature. We recommend 2 additions to the MeSH Tree Structure and 6 new MeSH headings.

Changing Vocabularies: A Guide to Help Bioethics Searchers Find Relevant Literature in National Library of Medicine Databases Using the Medical Subject Headings (MeSH) Indexing Vocabulary

Changing Vocabularies:A Guide to Help Bioethics Searchers Find Relevant Literature in National Library of Medicine Databases Using the Medical Subject Headings (MeSH) Indexing Vocabulary Tamar Joy Kahn and Hannelore Ninomiya Table of Contents I. Introduction II. Accessing and Searching MEDLINE/PubMed II-A. Using the Bioethics Subset Limiter in MEDLINE/PubMed II-B. Searching MEDLINE Using Free Text or MeSH Headings II-C. MeSH Subheadings, with Advice About Their Use for Bioethics-Related Retrieval II-D. MeSH Geographics and "Check Tags," with Advice About Their Use for Bioethics-Related Retrieval II-E. Bioethics-Related "Other Terms" III. Accessing and Searching the LOCATORplus Database III-A. Limiting Retrieval to Bioethics-Related Materials in LOCATORplus III-B. Searching LOCATORplus Using Free Text or MeSH Headings IV. Using the NLM Gateway for Searching Simultaneously MEDLINE/PubMed, LOCATORplus, and Other NLM Databases IV-A. Using the Bioethics Subset Limiter with the Gateway IV-B. Searching the Gateway Using Free Text or MeSH Headings V. Hints for Designing Bioethics Subject Search Strategies in MEDLINE/PubMed, LOCATORplus, and the Gateway VI. Selected "Major Issue" Bioethics Thesaurus Keywords and Corresponding MeSH, Arranged by Broad Subject Category Appendix A: Quick Guide to Finding Bioethics Citations in MEDLINE/PubMed Appendix B: Quick Guide to Finding Bioethics Citations in LOCATORplus Appendix C: Quick Guide to Finding Bioethics Citations Using the NLM Gateway Appendix D: Alphabetical List of All Bioethics Thesaurus Keywords with Corresponding MeSH Terms (not included here; available at http://bioethics.georgetown.edu/ir) I. Introduction For over twenty years, through December 2000, the National Library of Medicine (NLM) provided access to the journal and monographic literature of bioethics via a specialized database, BIOETHICSLINE, which was produced by the Kennedy Institute of Ethics (KIE) at Georgetown University. BIOETHICSLINE was indexed and searched using Keywords from the controlled vocabulary of the KIE's Bioethics Thesaurus. The vocabulary of the Thesaurus was developed over many years to reflect the concepts and terminology encountered in the bioethics literature. Subsequently, citations to the journal literature were integrated into the "Bioethics Subset" of NLM's MEDLINE/PubMed database, which is made available on the World Wide Web via the PubMed retrieval system. Citations to books, book chapters, and audiovisual materials were integrated into NLM's Web-based database, LOCATORplus. Both of these large databases are indexed with NLM's MeSH (Medical Subject Headings)vocabulary. Most of the approximately 750 Bioethics Thesaurus Keywords have been integrated into the MeSH vocabulary, which contains nearly 22,000 "subject descriptors." The form of many Keywords was modified to fit the language patterns and hierarchical "tree" structures of MeSH or to avoid ambiguity in the broad medical context. For example, a natural language order multiword Keyword (MEDICAL ETHICS) became an inverted order MeSH Heading (ETHICS, MEDICAL). In nearly all such cases, the Keyword has been programmed as an "entry" term to the MeSH Heading, making it possible to search MEDLINE/PubMed with either the natural language or inverted form. This Guide, available on the World Wide Web at http://bioethics.georgetown.edu/ir, is designed to help searchers familiar with the bioethics literature, or with the subject Keywords of the Bioethics Thesaurus, navigate the MeSH vocabulary to find terms that are likely to retrieve relevant citations. The Guide is also designed to introduce people interested in bioethics to the MEDLINE/PubMed and LOCATORplus databases and to NLM's Gateway system, which lets users search simultaneously in multiple NLM databases with their differing retrieval systems. See Section VI for a set of easy-to-scan lists, in broad subject categories, of a subset of 110 Keywords that represent "major issues" in bioethics. In each list, Column 1 shows the Keyword (KW) and Column 2 shows the MeSH Headings (MH) most likely to have been used to index the same concept in MEDLINE/PubMed and LOCATORplus. Appendix D (available at http://bioethics.georgetown.edu/ir) provides a full alphabetical list of all Bioethics Thesaurus Keywords that have MeSH equivalents. [End Page 276] II. Accessing and Searching MEDLINE/Pubmed The MEDLINE/PubMed database is available free on the Internet, at http://pubmed.gov. For detailed information on how to search the database...

STL2/386: HONselect: A new assisted-search facility for health information and resources integrating heterogeneous databases

IntroductionUsers looking for medical and health information today can investigate a widening assortment of sources. A full package of information on a specific subject can include descriptions of a disease or a condition, relevant articles from scientific journals, articles from a variety of popular newsletters, pages from different Web sites, links to support groups, patient advocates, conferences, continuing medical education, still and moving images and much more. However, finding and collating all this material is time-consuming, for the user must first painstakingly compile it from disparate sources. In response to growing user demand, HON has developed a new integrated search tool, HONselect, that helps meet the evolving needs of all users and is now available on the HON Web siteMethodsThe HONselect tool integrates the Medical Subject Headings (MeSH) terminology thesaurus, MEDLINE for scientific publication abstracts, HON's own MedHunt search engine for Web resources, the Newspage service for daily news and HON's Media Gallery for medical images. HONselect not only integrates the user's search for Internet information types and databases, it also offers a selection of resources available in each database. The National Library of Medicine (NLM) pioneered the concept with MEDLINEPlus which consists of a complementary resources selection such as Web sites and bibliographical references dedicated to patientsResultsThe HONselect tool enables the user to search different databases and to centralise the results in one interface as well as assist in finding the appropriate medical terms or MeSH headings by browsing or searching the MeSH hierarchical structure. But HONselect does more than add value to MEDLINE Plus. It, is a new concept which gives users the possibility of learning more about a given condition or disease in a scientifically structured fashion. in French and English, HONselect permits searching for medical words in natural language and finding the related scientific term, synonyms and definitions as well as their position in the hierarchical MeSH structure with both broader and narrower concepts and terms .DiscussionThe Internet has always provided different types of information on a number of different subjects. This fact is particularly obvious in the medical domain. Until now, persons looking for details on a specific disease on the Web will first use a search engine that is either general or specialised in the medical domain. They will then select the Web site that appears best suited to their needs. But finding news, scientific papers or images usually means extending the search to further databases . HONselect is the solution (http://www.hon.ch/MeSH2/)