Medical Subject Headings Indexing Research Articles

NLM-Chem-BC7: manually annotated full-text resources for chemical entity annotation and indexing in biomedical articles.

The automatic recognition of chemical names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The task is even more challenging when considering the identification of these entities in the article's full text and, furthermore, the identification of candidate substances for that article's metadata [Medical Subject Heading (MeSH) article indexing]. The National Library of Medicine (NLM)-Chem track at BioCreative VII aimed to foster the development of algorithms that can predict with high quality the chemical entities in the biomedical literature and further identify the chemical substances that are candidates for article indexing. As a result of this challenge, the NLM-Chem track produced two comprehensive, manually curated corpora annotated with chemical entities and indexed with chemical substances: the chemical identification corpus and the chemical indexing corpus. The NLM-Chem BioCreative VII (NLM-Chem-BC7) Chemical Identification corpus consists of 204 full-text PubMed Central (PMC) articles, fully annotated for chemical entities by 12 NLM indexers for both span (i.e.named entity recognition) and normalization (i.e.entity linking) using MeSH. This resource was used for the training and testing of the Chemical Identification task to evaluate the accuracy of algorithms in predicting chemicals mentioned in recently published full-text articles. The NLM-Chem-BC7 Chemical Indexing corpus consists of 1333 recently published PMC articles, equipped with chemical substance indexing by manual experts at the NLM. This resource was used for the evaluation of the Chemical Indexing task, which evaluated the accuracy of algorithms in predicting the chemicals that should be indexed, i.e.appear in the listing of MeSH terms for the document. This set was further enriched after the challenge in two ways: (i) 11 NLM indexers manually verified each of the candidate terms appearing in the prediction results of the challenge participants, but not in the MeSH indexing, and the chemical indexing terms appearing in the MeSH indexing list, but not in the prediction results, and (ii) the challenge organizers algorithmically merged the chemical entity annotations in the full text for all predicted chemical entities and used a statistical approach to keep those with the highest degree of confidence. As a result, the NLM-Chem-BC7 Chemical Indexing corpus is a gold-standard corpus for chemical indexing of journal articles and a silver-standard corpus for chemical entity identification in full-text journal articles. Together, these resources are currently the most comprehensive resources for chemical entity recognition, and we demonstrate improvements in the chemical entity recognition algorithms. We detail the characteristics of these novel resources and make them available for the community. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/NLM-Chem-BC7-corpus/.

Overdiagnosis: it’s official

Recently the US National Library of Medicine (NLM) unveiled new terms to be included in the medical subject headings (MeSH) vocabulary used to index, catalogue, and search journal articles, books,...

MEDLINE Complete

MEDLINE Complete is an EBSCO database which offers an alternative interface for accessing MEDLINE, and includes full-text of over 2,000 of the journals included in MEDLINE. As such, it includes primarily academic journals with some gray literature from government agencies and NGOs such as the CDC (Center for Disease Control and Prevention) and the WHO (World Health Organization). MEDLINE Complete includes MeSH (Medical Subject Heading) indexing and filters similar to those provided in PubMed, but the filters are not as well integrated into the interface as they are in PubMed. The search interface and indexing also have some quirks that librarians will want to be aware of. The main value-added feature of MEDLINE Complete, however, is the full-text access it provides, and this is likely to be the deciding factor for purchase. For libraries that subscribe to EBSCO’s MEDLINE with Full-text (a version of the database with full-text access to fewer publications) or are interested in acquiring integrated full-text access to a large group of biomedical journals, MEDLINE Complete may be appealing. On the other hand, MEDLINE Complete is less friendly to advanced medical searching than PubMed is. Depending on the existing full-text resources and patron needs at individual libraries, MEDLINE Complete may or may not be an improvement over integrating full-text from other vendors into the PubMed interface.

MeSHProbeNet-P

Indexing biomedical research articles with Medical Subject Headings (MeSH) can greatly facilitate biomedical research and information retrieval. Currently MeSH indexing is performed by human experts. To alleviate the time consumption and monetary cost caused by manual indexing, many automatic MeSH indexing models have been developed, such as MeSHProbeNet, DeepMeSH, and NLM’s official model Medical Text Indexer. In this article, we propose an end-to-end framework, MeSHProbeNet-P, which extends MeSHProbeNet with personalizable MeSH probes. In MeSHProbeNet-P, each MeSH probe carries certain aspects of biomedical knowledge and extracts related information from input articles. MeSHProbeNet-P is able to automatically personalize its MeSH probes for different input articles to ensure that the current MeSH probes best fit the current input article and the most informative features can be extracted from the article. We demonstrate the effectiveness of MeSHProbeNet-P in a real-world large-scale MeSH indexing challenge. MeSHProbeNet-P won the first place in the first batch of Task A in the 2019 BioASQ challenge. The result on the first test set of the challenge is reported in this article. We also provide ablation studies to show the advantages of personalizable MeSH probes.

BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text.

With the rapid increase of biomedical articles, large-scale automatic Medical Subject Headings (MeSH) indexing has become increasingly important. FullMeSH, the only method for large-scale MeSH indexing with full text, suffers from three major drawbacks: FullMeSH (i) uses Learning To Rank, which is time-consuming, (ii) can capture some pre-defined sections only in full text and (iii) ignores the whole MEDLINE database. We propose a computationally lighter, full text and deep-learning-based MeSH indexing method, BERTMeSH, which is flexible for section organization in full text. BERTMeSH has two technologies: (i) the state-of-the-art pre-trained deep contextual representation, Bidirectional Encoder Representations from Transformers (BERT), which makes BERTMeSH capture deep semantics of full text. (ii) A transfer learning strategy for using both full text in PubMed Central (PMC) and title and abstract (only and no full text) in MEDLINE, to take advantages of both. In our experiments, BERTMeSH was pre-trained with 3 million MEDLINE citations and trained on ∼1.5 million full texts in PMC. BERTMeSH outperformed various cutting-edge baselines. For example, for 20K test articles of PMC, BERTMeSH achieved a Micro F-measure of 69.2%, which was 6.3% higher than FullMeSH with the difference being statistically significant. Also prediction of 20K test articles needed 5min by BERTMeSH, while it took more than 10h by FullMeSH, proving the computational efficiency of BERTMeSH. Supplementary data are available at Bioinformatics online.

FullMeSH: improving large-scale MeSH indexing with full text.

With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as Medical Text Indexer, MeSHLabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is hampered by using limited information, i.e. only the title and abstract of biomedical articles. We propose FullMeSH, a large-scale MeSH indexing method taking advantage of the recent increase in the availability of full text articles. Compared to DeepMeSH and other state-of-the-art methods, FullMeSH has three novelties: (i) Instead of using a full text as a whole, FullMeSH segments it into several sections with their normalized titles in order to distinguish their contributions to the overall performance. (ii) FullMeSH integrates the evidence from different sections in a 'learning to rank' framework by combining the sparse and deep semantic representations. (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which achieves better performance on infrequent MeSH headings. FullMeSH has been developed and empirically trained on the entire set of 1.4 million full-text articles in the PubMed Central Open Access subset. It achieved a Micro F-measure of 66.76% on a test set of 10000 articles, which was 3.3% and 6.4% higher than DeepMeSH and MeSHLabeler, respectively. Furthermore, FullMeSH demonstrated an average improvement of 4.7% over DeepMeSH for indexing Check Tags, a set of most frequently indexed MeSH headings. The software is available upon request. Supplementary data are available at Bioinformatics online.

MeSHProbeNet: a self-attentive probe net for MeSH indexing.

MEDLINE is the primary bibliographic database maintained by National Library of Medicine (NLM). MEDLINE citations are indexed with Medical Subject Headings (MeSH), which is a controlled vocabulary curated by the NLM experts. This greatly facilitates the applications of biomedical research and knowledge discovery. Currently, MeSH indexing is manually performed by human experts. To reduce the time and monetary cost associated with manual annotation, many automatic MeSH indexing systems have been proposed to assist manual annotation, including DeepMeSH and NLM's official model Medical Text Indexer (MTI). However, the existing models usually rely on the intermediate results of other models and suffer from efficiency issues. We propose an end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms. Each MeSH probe enables the model to extract one specific aspect of biomedical knowledge from an input article, thus comprehensive biomedical information can be extracted with different MeSH probes and interpretability can be achieved at word level. MeSH terms are finally recommended with a unified classifier, making MeSHProbeNet both time efficient and space efficient. MeSHProbeNet won the first place in the latest batch of Task A in the 2018 BioASQ challenge. The result on the last test set of the challenge is reported in this paper. Compared with other state-of-the-art models, such as MTI and DeepMeSH, MeSHProbeNet achieves the highest scores in all the F-measures, including Example Based F-Measure, Macro F-Measure, Micro F-Measure, Hierarchical F-Measure and Lowest Common Ancestor F-measure. We also intuitively show how MeSHProbeNet is able to extract comprehensive biomedical knowledge from an input article.

MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing.

The US National Library of Medicine (NLM) uses the Medical Subject Headings (MeSH) (see Note 1 ) to index almost all 24 million citations in MEDLINE, which greatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28,000 MeSH terms. For the citation side, all existing methods, including Medical Text Indexer (MTI) by NLM, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. To solve these two challenges, we developed the MeSHLabeler and DeepMeSH. By utilizing "learning to rank" (LTR) framework, MeSHLabeler integrates multiple types of information to solve the challenge in the MeSH side, while DeepMeSH integrates deep semantic representation to solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at http://datamining-iip.fudan.edu.cn/deepmesh .

DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well.Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation.Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations.Availability and Implementation: The software is available upon request.Contact: zhusf@fudan.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Analyzing the Moving Parts of a Large-Scale Multi-Label Text Classification Pipeline: Experiences in Indexing Biomedical Articles.

Medical subject headings (MeSH) is a controlled hierarchical vocabulary used by the National Library of Medicine (NLM) to index biomedical articles. In the 2014 version of MeSH terminology there are a total of 27,149 terms. Librarians at the NLM tag each biomedical article to be indexed for the PubMed literature search system with terms from MeSH. This means the human indexers look at each article's full text and index it with a small set of descriptors, 13 on average, from over 27,000 descriptors available in MeSH. There have been many recent attempts to automate this process focused on using the article title and abstract text to predict MeSH terms for the corresponding article. There has also been an open automated biomedical indexing challenge, BioASQ [1], that started in 2013. The best general supervised learning framework in these challenges has been a pipeline with four different components: 1. pre-processing and feature extraction; 2. employing the binary relevance and/or nearest neighbor approaches to select a set of candidate terms; 3. ranking these candidate terms using corresponding informative features; and 4. applying label calibration to dynamically predict the number of top terms to be included in the final selection for the current instance. The specific details in how each of these components is implemented determines the performance variations of various entries in the challenge. In this paper, we analyze these moving parts of the MeSH indexing multi-label classification pipeline with experiments involving different combinations. Our best combination achieves ≈ 1% increase in micro F-score compared with the top performing team across the five weeks of the final batch of the BioASQ 2014 challenge. The main take away from our efforts is that small improvements/modifications to different components of the pipeline can offer moderate improvements to the overall performance of the method. Our experiences show that, at least thus far, top performances have resulted mostly due to these improvements rather than drastic changes of the core methodology.

MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence.

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ‘learning to rank’. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy.Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI.Availability and implementation: The software is available upon request.Contact: zhusf@fudan.edu.cn

Delay in indexing articles published in major pharmacy practice journals

The delay in time from entry in the PubMed database to indexing with medical subject heading (MeSH) terms for articles published in three major pharmacy practice journals was evaluated. In April 2013, MEDLINE data were retrieved for articles published in the American Journal of Health-System Pharmacy (AJHP), the Annals of Pharmacotherapy, and Pharmacotherapy between January 1, 2010, and December 31, 2011. Data collected for each article included the PubMed entry date, MeSH indexing date, and publication type. The PubMed entry date was defined as the Entrez date, the date the citation was added to the PubMed database. Medians and interquartile ranges (IQRs) were calculated for the time to indexing of articles and for the age of unindexed articles. The proportion of unindexed articles was also calculated. A total of 1626 publications were reviewed. Overall, the median time to indexing with MeSH terms was 114 days (IQR, 98-141 days): 107 days (IQR, 94-129 days) for AJHP, 131 days (IQR, 104-157 days) for Annals of Pharmacotherapy, and 114 days (IQR, 99-128 days) for Pharmacotherapy. The median age of unindexed articles was 807 days (IQR, 671-807 days). An analysis of three major pharmacy practice journals showed that the median time to indexing articles published in 2010 and 2011 was 114 days. While all articles from AJHP and Pharmacotherapy were indexed, 40 articles from Annals of Pharmacotherapy remained unindexed.

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.

Adherence to ICCS nomenclature guidelines in subsequent literature: A bibliometric study

Since the publication of the 2006 International Children's Continence Society (ICCS) guidelines on terminologies for lower urinary tract dysfunction in children, little is known of their impact. In this study, we aim to quantify the adherence to the guidelines in the published literature, and to examine whether Medical Subject Headings (MeSH) in MEDLINE reflect the recommended "new" ICCS terminology. Seven pairs of pre-specified paired terms (obsolete and recommended by the ICCS) were searched, limited to paediatric literature published between 2002 and 2010. Their use in the literature was compared between the pre-guideline (2002-2005) and post-guideline (2007-2010) period and across geographical regions. MeSH in MEDLINE were examined for the use of ICCS preferred terminology. Publications in paediatric urinary incontinence have shown a 49% increase from 2002-2005 to 2007-2010 (55-82 per year). There was about a fourfold increase in the likelihood of usage of ICCS recommended terminologies post ICCS guideline publication (OR: 4.19, 95% CI: 3.04-5.78, P < 0.001). Approximately 25% of the studies published between 2007 and 2010 used obsolete terminologies. Analysis indicated satisfactory uptake for most terms, with the exception of "urotherapy." There was no significant geographical variation in uptake. More than half of the ICCS-recommended terms (4/7) did not appear in the current MeSH indexing tree and scope notes. Overall uptake of recommended terms following release of ICCS terminology guidelines was encouraging although it remains suboptimal for certain terms. Efforts need to be made to improve the current MEDLINE indexing so that MeSH terms reflect terminology recommended by the ICCS.

Improving information retrieval using Medical Subject Headings Concepts: a test case on rare and chronic diseases

As more scientific work is published, it is important to improve access to the biomedical literature. Since 2000, when Medical Subject Headings (MeSH) Concepts were introduced, the MeSH Thesaurus has been concept based. Nevertheless, information retrieval is still performed at the MeSH Descriptor or Supplementary Concept level. The study assesses the benefit of using MeSH Concepts for indexing and information retrieval. Three sets of queries were built for thirty-two rare diseases and twenty-two chronic diseases: (1) using PubMed Automatic Term Mapping (ATM), (2) using Catalog and Index of French-language Health Internet (CISMeF) ATM, and (3) extrapolating the MEDLINE citations that should be indexed with a MeSH Concept. Type 3 queries retrieve significantly fewer results than type 1 or type 2 queries (about 18,000 citations versus 200,000 for rare diseases; about 300,000 citations versus 2,000,000 for chronic diseases). CISMeF ATM also provides better precision than PubMed ATM for both disease categories. Using MeSH Concept indexing instead of ATM is theoretically possible to improve retrieval performance with the current indexing policy. However, using MeSH Concept information retrieval and indexing rules would be a fundamentally better approach. These modifications have already been implemented in the CISMeF search engine.

MeSH 시소러스를 이용한 한영 교차언어 키워드 자동 부여

The medical thesaurus, MeSH (Medical Subject Heading), has been used as a controlled vocabulary thesaurus for English medical paper indexing for a long time. In this paper, we propose an automatic cross language keyword assignment method, which assigns English MeSH index terms to the abstract of a Korean medical paper. We compare the performance with the indexing performance of human indexers and the authors. The procedure of index term assignment is that first extracting Korean MeSH terms from text, changing these terms into the corresponding English MeSH terms, and calculating the importance of the terms to find the highest rank terms as the keywords. For the process, an effective method to solve spacing variants problem is proposed. Experiment showed that the method solved the spacing variant problem and reduced the thesaurus space by about 42%. And the experiment also showed that the performance of automatic keyword assignment is much less than that of human indexers but is as good as that of authors.

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.

Interpreting cost analyses of clinical interventions.

In the present era of cost containment, physicians need reliable data about specific interventions. The objectives of this study were to assist practitioners in interpretation of economic analyses and estimation of their own costs of implementing recommended interventions. MEDLINE search from 1966 through 1995 using the text words cost or expense and medical subject heading (MeSH) terms costs and cost analysis, cost control, cost of illness, cost savings, or cost-benefit analysis. The 4 eligibility criteria were clinical trial with random assignment; health care quality improvement intervention tested; effects measured on the process or outcome of care; and cost calculation mentioned in the report. After independent abstraction and after consensus development, financial data were entered into a costing protocol to determine which costs related to the intervention were provided. Of 181 articles, 97 (53.6%) included actual numbers on the costs of the intervention. Of 97 articles analyzed, the most frequently reported cost figures were in the category of operating expenses (direct cost, 61.9%; labor, 42.3%; and supplies, 32.0%). General overhead was not presented in 91 (93.8%) of the 97 studies. Only 14 (14.4%) of the 97 studies mentioned start-up costs. The text word $ in the abstract and the most useful MeSH index term of cost-benefit analysis appeared with nearly equal frequency in the articles that included actual cost data (37.1 % vs 35.1%). Two thirds of articles indexed with the MeSH term cost control did not include cost figures. Statements regarding cost without substantiating data are made habitually in reports of clinical trials. In clinical trial reports presenting data on expenditures, start-up costs and general overhead are frequently disregarded. Practitioners can detect missing information by placing cost data in a standardized protocol. The costing protocol of this study can help bridge care delivery and economic analyses.

Creative ways to locate MeSH subheading information.

Locating notes about MeSH subheadings can be one of the most difficult aspects of MEDLINE searching. When used with Medical Subject Headings (MeSH), subheadings serve to limit or define the focus of a heading. National Library of Medicine (NLM) indexers, staff members who enter the MeSH indexing into the database, use subheadings freely. They do follow indexing conventions and savy searchers need to be aware of how to obtain basic subheading information to assist in search formulation. There are a number of methods which may be used in order to view subheading use notes. This column will provide an overview of some alternatives to locating such information.