Comparing Medical Term Usage Patterns of Professionals and Search Engine and Community Question Answering Service Users in Japan: Log Analysis.

BACKGROUND: Despite increasing opportunities for acquiring health information online, discussion of the specific words used in searches has been limited. OBJECTIVE: The aim of this study was to clarify the medical information gap between medical professionals and the general public in Japan through health information-seeking activities on the internet. METHODS: Search and posting data were analyzed from one of the most popular domestic search engines in Japan (Yahoo! JAPAN Search) and the most popular Japanese community question answering service (Yahoo! Chiebukuro). We compared the frequency of 100 clinical words appearing in the clinical case reports of medical professionals (clinical frequency) with their frequency in Yahoo! JAPAN Search (search frequency) logs and questions posted to Yahoo! Chiebukuro (question frequency). The Spearman correlation coefficient was used to quantify association patterns among the three information sources. Additionally, user information (gender and age) in the search frequency associated with each registered user was extracted. RESULTS: Significant correlations were observed between clinical and search frequencies (r=0.29, P=.003), clinical and question frequencies (r=0.34, P=.001), and search and question frequencies (r=0.57, P<.001). Low-frequency words in clinical frequency (eg, "hypothyroidism," "ulcerative colitis") highly ranked in search frequency. Similarly, "pain," "slight fever," and "numbness" were highly ranked only in question frequency. The weighted average of ages was 34.5 (SD 2.7) years, and the weighted average of gender (man -1, woman +1) was 0.1 (SD 0.1) in search frequency. Some words were specifically extracted from the search frequency of certain age groups, including "abdominal pain" (10-20 years), "plasma cells" and "inflammatory findings" (20-30 years), "DM" (diabetes mellitus; 30-40 years), "abnormal shadow" and "inflammatory findings" (40-50 years), "hypertension" and "abnormal shadow" (50-60 years), and "lung cancer" and "gastric cancer" (60-70 years). CONCLUSIONS: Search and question frequencies showed similar tendencies, whereas search and clinical frequencies showed discrepancy. Low-clinical frequency words related to diseases such as "hypothyroidism" and "ulcerative colitis" had high search frequencies, whereas those related to symptoms such as "pain," "slight fever," and "numbness" had high question frequencies. Moreover, high search frequency words included designated intractable diseases such as "ulcerative colitis," which has an incidence of less than 0.1% in the Japanese population. Therefore, it is generally worthwhile to pay attention not only to major diseases but also to minor diseases that users frequently seek information on, and more words will need to be analyzed in the future. Some characteristic words for certain age groups were observed (eg, 20-40 years: "cancer"; 40-60 years: diagnoses and diseases identified in health examinations; 60-70 years: diseases with late adulthood onset and "death"). Overall, this analysis demonstrates that medical professionals as information providers should be aware of clinical frequency, and medical information gaps between professionals and the general public should be bridged.

Search results outliers among MEDLINE platforms.

OBJECTIVE: Hypothetically, content in MEDLINE records is consistent across multiple platforms. Though platforms have different interfaces and requirements for query syntax, results should be similar when the syntax is controlled for across the platforms. The authors investigated how search result counts varied when searching records among five MEDLINE platforms. METHODS: We created 29 sets of search queries targeting various metadata fields and operators. Within search sets, we adapted 5 distinct, compatible queries to search 5 MEDLINE platforms (PubMed, ProQuest, EBSCOhost, Web of Science, and Ovid), totaling 145 final queries. The 5 queries were designed to be logically and semantically equivalent and were modified only to match platform syntax requirements. We analyzed the result counts and compared PubMed's MEDLINE result counts to result counts from the other platforms. We identified outliers by measuring the result count deviations using modified z-scores centered around PubMed's MEDLINE results. RESULTS: Web of Science and ProQuest searches were the most likely to deviate from the equivalent PubMed searches. EBSCOhost and Ovid were less likely to deviate from PubMed searches. Ovid's results were the most consistent with PubMed's but appeared to apply an indexing algorithm that resulted in lower retrieval sets among equivalent searches in PubMed. Web of Science exhibited problems with exploding or not exploding Medical Subject Headings (MeSH) terms. CONCLUSION: Platform enhancements among interfaces affect record retrieval and challenge the expectation that MEDLINE platforms should, by default, be treated as MEDLINE. Substantial inconsistencies in search result counts, as demonstrated here, should raise concerns about the impact of platform-specific influences on search results.

Evaluating alignment quality between iconic language and reference terminologies using similarity metrics.

BACKGROUND: Visualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases - tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR. METHODS: For medical literature retrieval purposes and EHR browsing, the MeSH thesaurus and the ICD10, both organized hierarchically, were aligned to VCM language. Some MeSH to VCM alignments were performed automatically but others were performed manually and validated. ICD10 to VCM alignment was entirely manually performed. Inter-alignment agreement was assessed on ICD10 codes and MeSH descriptors, sharing the same Concept Unique Identifiers in the Unified Medical Language System (UMLS). Three metrics were used to compare two VCM icons: binary comparison, crude Dice Similarity Coefficient (DSCcrude), and semantic Dice Similarity Coefficient (DSCsemantic), based on Lin similarity. An analysis of discrepancies was performed. RESULTS: MeSH to VCM alignment resulted in 10,783 relations: 1,830 of which were manually performed and 8,953 were automatically inherited. ICD10 to VCM alignment led to 19,852 relations. UMLS gathered 1,887 alignments between ICD10 and MeSH. Only 1,606 of them were used for this study. Inter-alignment agreement using only validated MeSH to VCM alignment was 74.2% [70.5-78.0]CI95%, DSCcrude was 0.93 [0.91-0.94]CI95%, and DSCsemantic was 0.96 [0.95-0.96]CI95%. Discrepancy analysis revealed that even if two thirds of errors came from the reviewers, UMLS was nevertheless responsible for one third. CONCLUSIONS: This study has shown strong overall inter-alignment agreement between MeSH to VCM and ICD10 to VCM manual alignments. VCM icons have now been integrated into a guideline search engine (http://www.cismef.org) and a health terminologies portal (http://www.hetop.eu).

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

BACKGROUND: 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. OBJECTIVE: The study assesses the benefit of using MeSH Concepts for indexing and information retrieval. METHODS: 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. RESULTS: 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. DISCUSSION: 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.

Controlled search term vocabularies for finding articles relevant to injury prevention and safety promotion.

OBJECTIVES: To assess the usefulness of the controlled vocabularies of PubMed/MEDLINE and PsycINFO for finding articles on injury prevention and safety promotion (IPSP) topics and to identify specific indexing problems that can contribute to incomplete retrieval. METHODS: Professional reference librarians provided search strategies for finding articles relevant to five topics pertaining to the injury prevention field in the two bibliographic databases. The results of implementing these search strategies were compared with the results of a presumptive gold standard-serial textword searches on the same topics. The index terms assigned to the articles that were missed by the librarian strategies were examined. RESULTS: The search products of the librarian-constructed search strategies identified 34-91% of the IPSP-relevant articles that were identified through serial textword searches of the two databases. Specific indexing issues were found to contribute to this loss. CONCLUSIONS: Librarians bring expertise to searching, but irregular or incomplete indexing can limit the product of even well-constructed searches for articles on IPSP topics.

The CLASSE GATOR (CLinical Acronym SenSE disambiGuATOR): A Method for predicting acronym sense from neonatal clinical notes.

OBJECTIVE: To develop an algorithm for identifying acronym 'sense' from clinical notes without requiring a clinically annotated training set. MATERIALS AND METHODS: Our algorithm is called CLASSE GATOR: Clinical Acronym SenSE disambiGuATOR. CLASSE GATOR extracts acronyms and definitions from PubMed Central (PMC). A logistic regression model is trained using words associated with specific acronym-definition pairs from PMC. CLASSE GATOR uses this library of acronym-definitions and their corresponding word feature vectors to predict the acronym 'sense' from Beth Israel Deaconess (MIMIC-III) neonatal notes. RESULTS: We identified 1,257 acronyms and 8,287 definitions including a random definition from 31,764 PMC articles on prenatal exposures and 2,227,674 PMC open access articles. The average number of senses (definitions) per acronym was 6.6 (min = 2, max = 50). The average internal 5-fold cross validation was 87.9 % (on PMC). We found 727 unique acronyms (57.29 %) from PMC were present in 105,044 neonatal notes (MIMIC-III). We evaluated the performance of acronym prediction using 245 manually annotated clinical notes with 9 distinct acronyms. CLASSE GATOR achieved an overall accuracy of 63.04 % and outperformed random for 8/9 acronyms (88.89 %) when applied to clinical notes. We also compared our algorithm with UMN's acronym set, and found that CLASSE GATOR outperformed random for 63.46 % of 52 acronyms when using logistic regression, 75.00 % when using Bert and 76.92 % when using BioBert as the prediction algorithm within CLASSE GATOR. CONCLUSIONS: CLASSE GATOR is the first automated acronym sense disambiguation method for clinical notes. Importantly, CLASSE GATOR does not require an expensive manually annotated acronym-definition corpus for training.

Publication trends in the medical informatics literature: 20 years of "Medical Informatics" in MeSH.

BACKGROUND: The purpose of this study is to identify publication output, and research areas, as well as descriptively and quantitatively characterize the field of medical informatics through publication trend analysis over a twenty year period (1987-2006). METHODS: A bibliometric analysis of medical informatics citations indexed in Medline was performed using publication trends, journal frequency, impact factors, MeSH term frequencies and characteristics of citations. RESULTS: There were 77,023 medical informatics articles published during this 20 year period in 4,644 unique journals. The average annual article publication growth rate was 12%. The 50 identified medical informatics MeSH terms are rarely assigned together to the same document and are almost exclusively paired with a non-medical informatics MeSH term, suggesting a strong interdisciplinary trend. Trends in citations, journals, and MeSH categories of medical informatics output for the 20-year period are summarized. Average impact factor scores and weighted average impact factor scores increased over the 20-year period with two notable growth periods. CONCLUSION: There is a steadily growing presence and increasing visibility of medical informatics literature over the years. Patterns in research output that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline, and highlight specific journals in which the medical informatics literature appears most frequently, including general medical journals as well as informatics-specific journals.

A case study: using social tagging to engage students in learning Medical Subject Headings.

In exploring new ways of teaching students how to use Medical Subject Headings (MeSH), librarians at Boston University's Alumni Medical Library (AML) integrated social tagging into their instruction. These activities were incorporated into the two-credit graduate course, "GMS MS 640: Introduction to Biomedical Information," required for all students in the graduate medical science program. Hands-on assignments and in-class exercises enabled librarians to present MeSH and the concept of a controlled vocabulary in a familiar and relevant context for the course's Generation Y student population and provided students the opportunity to actively participate in creating their education. At the conclusion of these activities, students were surveyed regarding the clarity of the presentation of the MeSH vocabulary. Analysis of survey responses indicated that 46% found the concept of MeSH to be the clearest concept presented in the in-class intervention.

Mapping the knowledge structure and trends of epilepsy genetics over the past decade: A co-word analysis based on medical subject headings terms.

INTRODUCTION: Over the past 10 years, epilepsy genetics has made dramatic progress. This study aimed to analyze the knowledge structure and the advancement of epilepsy genetics over the past decade based on co-word analysis of medical subject headings (MeSH) terms. METHODS: Scientific publications focusing on epilepsy genetics from the PubMed database (January 2009-December 2018) were retrieved. Bibliometric information was analyzed quantitatively using Bibliographic Item Co-Occurrence Matrix Builder (BICOMB) software. A knowledge social network analysis and publication trend based on the high-frequency MeSH terms was built using VOSviewer. RESULTS: According to the search strategy, a total of 5185 papers were included. Among all the extracted MeSH terms, 86 high-frequency MeSH terms were identified. Hot spots were clustered into 5 categories including: "ion channel diseases," "beyond ion channel diseases," "experimental research & epigenetics," "single nucleotide polymorphism & pharmacogenetics," and "genetic techniques". "Epilepsy," "mutation," and "seizures," were located at the center of the knowledge network. "Ion channel diseases" are typically in the most prominent position of epilepsy genetics research. "Beyond ion channel diseases" and "genetic techniques," however, have gradually grown into research cores and trends, such as "intellectual disability," "infantile spasms," "phenotype," "exome," " deoxyribonucleic acid (DNA) copy number variations," and "application of next-generation sequencing." While ion channel genes such as "SCN1A," "KCNQ2," "SCN2A," "SCN8A" accounted for nearly half of epilepsy genes in MeSH terms, a number of additional beyond ion channel genes like "CDKL5," "STXBP1," "PCDH19," "PRRT2," "LGI1," "ALDH7A1," "MECP2," "EPM2A," "ARX," "SLC2A1," and more were becoming increasingly popular. In contrast, gene therapies, treatment outcome, and genotype-phenotype correlations were still in their early stages of research. CONCLUSION: This co-word analysis provides an overview of epilepsy genetics research over the past decade. The 5 research categories display publication hot spots and trends in epilepsy genetics research which could consequently supply some direction for geneticists and epileptologists when launching new projects.

Analysis of queries sent to PubMed at the point of care: observation of search behaviour in a medical teaching hospital.

BACKGROUND: The use of PubMed to answer daily medical care questions is limited because it is challenging to retrieve a small set of relevant articles and time is restricted. Knowing what aspects of queries are likely to retrieve relevant articles can increase the effectiveness of PubMed searches. The objectives of our study were to identify queries that are likely to retrieve relevant articles by relating PubMed search techniques and tools to the number of articles retrieved and the selection of articles for further reading. METHODS: This was a prospective observational study of queries regarding patient-related problems sent to PubMed by residents and internists in internal medicine working in an Academic Medical Centre. We analyzed queries, search results, query tools (Mesh, Limits, wildcards, operators), selection of abstract and full-text for further reading, using a portal that mimics PubMed. RESULTS: PubMed was used to solve 1121 patient-related problems, resulting in 3205 distinct queries. Abstracts were viewed in 999 (31%) of these queries, and in 126 (39%) of 321 queries using query tools. The average term count per query was 2.5. Abstracts were selected in more than 40% of queries using four or five terms, increasing to 63% if the use of four or five terms yielded 2-161 articles. CONCLUSION: Queries sent to PubMed by physicians at our hospital during daily medical care contain fewer than three terms. Queries using four to five terms, retrieving less than 161 article titles, are most likely to result in abstract viewing. PubMed search tools are used infrequently by our population and are less effective than the use of four or five terms. Methods to facilitate the formulation of precise queries, using more relevant terms, should be the focus of education and research.

An exploratory analysis of PubMed's free full-text limit on citation retrieval for clinical questions.

OBJECTIVE: The research sought to determine (1) how use of the PubMed free full-text (FFT) limit affects citation retrieval and (2) how use of the FFT limit impacts the types of articles and levels of evidence retrieved. METHODS: Four clinical questions based on a research agenda for physical therapy were searched in PubMed both with and without the use of the FFT limit. Retrieved citations were examined for relevancy to each question. Abstracts of relevant citations were reviewed to determine the types of articles and levels of evidence. Descriptive analysis was used to compare the total number of citations, number of relevant citations, types of articles, and levels of evidence both with and without the use of the FFT limit. RESULTS: Across all 4 questions, the FFT limit reduced the number of citations to 11.1% of the total number of citations retrieved without the FFT limit. Additionally, high-quality evidence such as systematic reviews and randomized controlled trials were missed when the FFT limit was used. CONCLUSIONS: Health sciences librarians play a key role in educating users about the potential impact the FFT limit has on the number of citations, types of articles, and levels of evidence retrieved.

A day in the life of PubMed: analysis of a typical day's query log.

OBJECTIVE: To characterize PubMed usage over a typical day and compare it to previous studies of user behavior on Web search engines. DESIGN: We performed a lexical and semantic analysis of 2,689,166 queries issued on PubMed over 24 consecutive hours on a typical day. MEASUREMENTS: We measured the number of queries, number of distinct users, queries per user, terms per query, common terms, Boolean operator use, common phrases, result set size, MeSH categories, used semantic measurements to group queries into sessions, and studied the addition and removal of terms from consecutive queries to gauge search strategies. RESULTS: The size of the result sets from a sample of queries showed a bimodal distribution, with peaks at approximately 3 and 100 results, suggesting that a large group of queries was tightly focused and another was broad. Like Web search engine sessions, most PubMed sessions consisted of a single query. However, PubMed queries contained more terms. CONCLUSION: PubMed's usage profile should be considered when educating users, building user interfaces, and developing future biomedical information retrieval systems.

Specific classification of elibrary resources says more about users' preferences.

BACKGROUND: Medical Subject Headings (MeSH) are a hierarchical taxonomy of over 42,000 descriptors designed to classify scientific literature; it is hierarchical with generic high order headings and specific low order headings. Over 1,000 resources in the Primary Care Electronic Library (PCEL - www.pcel.info) were classified with MeSH. METHODS: Each of the entries or resources in the primary care digital library was assigned up to five MeSH terms. We compared whether the most generic or specific MeSH term ascribed to each resource best predicted user preferences. RESULTS: over the four month period analysed statistically significant differences were found for resources according to specific key MeSH terms they were classified by. This result was not repeated for generic key MeSH terms. CONCLUSIONS: Analysis of the use of specific MeSH terms reveals user preferences that would have otherwise remained obscured. These preferences are not found if more generic MeSH terms are analysed.

A MeSH-based text mining method for identifying novel prebiotics.

Prebiotics contribute to the well-being of their host by altering the composition of the gut microbiota. Discovering new prebiotics is a challenging and arduous task due to strict inclusion criteria; thus, highly limited numbers of prebiotic candidates have been identified. Notably, the large numbers of published studies may contain substantial information attached to various features of known prebiotics that can be used to predict new candidates. In this paper, we propose a medical subject headings (MeSH)-based text mining method for identifying new prebiotics with structured texts obtained from PubMed. We defined an optimal feature set for prebiotics prediction using a systematic feature-ranking algorithm with which a variety of carbohydrates can be accurately classified into different clusters in accordance with their chemical and biological attributes. The optimal feature set was used to separate positive prebiotics from other carbohydrates, and a cross-validation procedure was employed to assess the prediction accuracy of the model. Our method achieved a specificity of 0.876 and a sensitivity of 0.838. Finally, we identified a high-confidence list of candidates of prebiotics that are strongly supported by the literature. Our study demonstrates that text mining from high-volume biomedical literature is a promising approach in searching for new prebiotics.

Use of scientific social networking to improve the research strategies of PubMed readers.

BACKGROUND: Keeping up with journal articles on a daily basis is an important activity of scientists engaged in biomedical research. Usually, journal articles and papers in the field of biomedicine are accessed through the Medline/PubMed electronic library. In the process of navigating PubMed, researchers unknowingly generate user-specific reading profiles that can be shared within a social networking environment. This paper examines the structure of the social networking environment generated by PubMed users. METHODS: A web browser plugin was developed to map [in Medical Subject Headings (MeSH) terms] the reading patterns of individual PubMed users. RESULTS: We developed a scientific social network based on the personal research profiles of readers of biomedical articles. A browser plugin is used to record the digital object identifier or PubMed ID of web pages. Recorded items are posted on the activity feed and automatically mapped to PubMed abstract. Within the activity feed a user can trace back previously browsed articles and insert comments. By calculating the frequency with which specific MeSH occur, the research interests of PubMed users can be visually represented with a tag cloud. Finally, research profiles can be searched for matches between network users. CONCLUSIONS: A social networking environment was created using MeSH terms to map articles accessed through the Medline/PubMed online library system. In-network social communication is supported by the recommendation of articles and by matching users with similar scientific interests. The system is available at http://bioknol.org/en/.

Developing optimal search strategies for finding information on herbs and other medicinal plants in MEDLINE.

The MEDLINE database is an important resource for locating up-to-date information on herbs and other botanical therapies. However, the evolving nature of complementary and alternative medicine (CAM) and the complexity of herbal terminology can make it difficult to identify useful citations. This paper describes optimal search strategies for finding clinical information on herbs and medicinal plants in MEDLINE using the PubMed retrieval system. Searchers will benefit from an understanding of the structure of Medical Subject Headings (MeSH) and PubMed's advanced search capabilities. Details for using PubMed's MeSH Database, Clinical Queries, Clipboard, and limiting features to retrieve pertinent botanical research are described. Tables containing MeSH terms for medicinal plants and useful print and electronic resources are included.