bims-librar Biomed News
on Biomedical librarianship
Issue of 2019‒03‒10
ten papers selected by
Thomas Krichel
Open Library Society
  1. Searching the literature for studies for a systematic review. Part 2: Resources for searching the medical literature.
  2. Applying Situational Theory of Problem Solving in Cancer Information Seeking: A Cross-Sectional Analysis of 2014 HINTS Survey.
  3. Accuracy, completeness and accessibility of online information on fibromyalgia.
  4. A Guide to Dictionary-Based Text Mining.
  5. Combining Text Mining and Computational Chemistry Visually and Semantically Enriches Analytical Chemistry Literature Searches.
  6. Text Mining for Drug Discovery.
  7. Learning to successfully search the scientific and medical literature.
  8. MeSHProbeNet: A Self-attentive Probe Net for MeSH Indexing.
  9. Patient-Targeted "Googling:" When Therapists Search for Information About Their Patients Online.
  10. Twenty "must-read" research articles for primary care providers in Nunavik: scoping study and development of an information tool.
  1. Am J Orthod Dentofacial Orthop. 2019 Mar;pii: S0889-5406(18)31085-0. [Epub ahead of print]155(3): 445-447
    Searching the literature for studies for a systematic review. Part 2: Resources for searching the medical literature.
    Anne Littlewood, Dimitrios Kloukos.
      
    DOI:  https://doi.org/10.1016/j.ajodo.2018.12.005
  2. J Health Commun. 2019 Mar 08. 1-9
    Applying Situational Theory of Problem Solving in Cancer Information Seeking: A Cross-Sectional Analysis of 2014 HINTS Survey.
    Hongmei Shen, Jie Xu, Yueyan Wang.
      Informed by the Situational Theory of Problem Solving (STOPS), this study used data from the Health Information National Trends Survey, a large and representative national sample, to examine predictors of information seeking and information accessing of health information, including cancer-related information. We found that the independent variables in STOPS-problem recognition, involvement recognition, and referent criterion- well predicted people's information seeking of cancer-related information and accessing of health information on line. However, the impact of trust in online health information was more complicated than anticipated. Our study demonstrated the utility of the STOPS in the health information context. Theoretical and practical implications are discussed.
    DOI:  https://doi.org/10.1080/10810730.2019.1587111
  3. Rheumatol Int. 2019 Mar 06.
    Accuracy, completeness and accessibility of online information on fibromyalgia.
    Deepika Basavakumar, Mirika Flegg, Jessica Eccles, Pietro Ghezzi.
      Fibromyalgia is a multi-factorial illness primarily characterised by widespread chronic pain and fatigue, with several symptoms and associated conditions. Due to a lack of clinical awareness and an absence of objective diagnostic measures, fibromyalgia patients often engage with online health information. The aim is to investigate the completeness and trustworthiness of the information available online on fibromyalgia. Google.co.uk was searched for 'fibromyalgia', the first 200 webpages were imported and 148 were analysed for standard health information quality criteria (JAMA score, HONcode) as well as completeness of information in terms of symptoms, causes and treatments mentioned. The most frequent typology of webpages was from health professionals (38%), with commercial websites being less frequent (7%). Overall, the quality, completeness and accessibility of online health information was poor. Completeness of coverage for symptoms, causes and associated conditions was especially lacking, with pages from not-for-profit organisations discussing the highest number of symptoms (median 8, min 0, max 11, interquartile range, IQR 4.5; n = 14) compared to the rest of the websites in the search engine results (median 4, min 0, max 11, IQR 4; n = 134). Mean readability was grade 9 (median 9, min 1, max 18, IQR 3), with only 8% websites meeting the recommended readability of grade 6. The Internet provides incomplete information on fibromyalgia, which does not fulfil the most queried aspect(s) by patients, symptoms, and may be difficult to understand by lay persons. Not-for-profit organisations provide the most complete information compared to other types of websites.
    Keywords:  Fibromyalgia; Information quality; Internet; Pain
    DOI:  https://doi.org/10.1007/s00296-019-04265-0
  4. Methods Mol Biol. 2019 ;1939 73-89
    A Guide to Dictionary-Based Text Mining.
    Helen V Cook, Lars Juhl Jensen.
      PubMed contains more than 27 million documents, and this number is growing at an estimated 4% per year. Even within specialized topics, it is no longer possible for a researcher to read any field in its entirety, and thus nobody has a complete picture of the scientific knowledge in any given field at any time. Text mining provides a means to automatically read this corpus and to extract the relations found therein as structured information. Having data in a structured format is a huge boon for computational efforts to access, cross reference, and mine the data stored therein. This is increasingly useful as biological research is becoming more focused on systems and multi-omics integration. This chapter provides an overview of the steps that are required for text mining: tokenization, named entity recognition, normalization, event extraction, and benchmarking. It discusses a variety of approaches to these tasks and then goes into detail on how to prepare data for use specifically with the JensenLab tagger. This software uses a dictionary-based approach and provides the text mining evidence for STRING and several other databases.
    Keywords:  Automated text processing; Dictionary-based approach; Named entity recognition; PubMed; Structured information; Text mining; Text normalization
    DOI:  https://doi.org/10.1007/978-1-4939-9089-4_5
  5. Anal Chem. 2019 Mar 05.
    Combining Text Mining and Computational Chemistry Visually and Semantically Enriches Analytical Chemistry Literature Searches.
    Magnus Palmblad.
      The open-access scientific literature contains a wealth of information for meaningful text mining. However, this information is not always easy to retrieve. This technical note addresses the problem by a new flexible method combining in a single workflow existing resources for literature search, text mining, and large-scale prediction of physicochemical and biological properties. The results are visualized as virtual mass spectra, chromatograms or images in styles new to text mining but familiar to analytical chemistry. The method is demonstrated on comparisons of analytical chemistry techniques and semantically enriched searches for proteins and their activities, but may also be of general utility in experimental design, drug discovery, chemical syntheses, business intelligence and historical studies. The method is realized in shareable scientific workflows using only freely available data, services and software that scale to millions of publications and named chemical entities in the literature.
    DOI:  https://doi.org/10.1021/acs.analchem.8b05818
  6. Methods Mol Biol. 2019 ;1939 231-252
    Text Mining for Drug Discovery.
    Si Zheng, Shazia Dharssi, Meng Wu, Jiao Li, Zhiyong Lu.
      Recent advances in technology have led to the exponential growth of scientific literature in biomedical sciences. This rapid increase in information has surpassed the threshold for manual curation efforts, necessitating the use of text mining approaches in the field of life sciences. One such application of text mining is in fostering in silico drug discovery such as drug target screening, pharmacogenomics, adverse drug event detection, etc. This chapter serves as an introduction to the applications of various text mining approaches in drug discovery. It is divided into two parts with the first half as an overview of text mining in the biosciences. The second half of the chapter reviews strategies and methods for four unique applications of text mining in drug discovery.
    Keywords:  Biomedical literature; Biomedical text mining; Deep learning; Drug discovery; Electronic medical records
    DOI:  https://doi.org/10.1007/978-1-4939-9089-4_13
  7. Cell Stress Chaperones. 2019 Mar 06.
    Learning to successfully search the scientific and medical literature.
    Emily A Thompson, Laurissa B Gann, Erik N K Cressman.
      Searching the literature is often overlooked and receives inadequate attention. In this article, we seek to address this issue by presenting several strategies. Here, five steps are outlined and discussed to facilitate effective literature searching.
    Keywords:  Autoalert; Citation mining; Database; Literature; Publication; Search; Search strategy
    DOI:  https://doi.org/10.1007/s12192-019-00984-2
  8. Bioinformatics. 2019 Mar 09. pii: btz142. [Epub ahead of print]
    MeSHProbeNet: A Self-attentive Probe Net for MeSH Indexing.
    Guangxu Xun, Kishlay Jha, Ye Yuan, Yaqing Wang, Aidong Zhang.
      MOTIVATION: 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 Deep-MeSH and NLM's official model Medical Text Indexer (MTI). However, the existing models usually rely on the inter-mediate 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.RESULTS: 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.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btz142
  9. Psychodyn Psychiatry. 2019 ;47(1): 27-38
    Patient-Targeted "Googling:" When Therapists Search for Information About Their Patients Online.
    Camila de Araujo Reinert, Clarice Kowacs.
      The ubiquitous nature of the internet and of online social networking has created new opportunities but also challenges for the psychotherapist. Former notions of anonymity and privacy are now infeasible as a result of massive information sharing through electronic media. The clinical repercussions of these changes are being extensively debated, but issues involving patient privacy and anonymity have not been sufficiently explored. Although several aspects of the impact of the internet on therapeutic setting-such as the need for psychotherapists to exercise caution when making personal information available online-have been addressed in the literature, there has been comparatively little discussion on psychotherapists seeking information about their patients on the internet, a phenomenon known as "patient-targeted googling" (PTG).
    Keywords:  boundary violations; enactment; patient-targeted googling; privacy; psychoanalytical psychotherapy; setting; social media
    DOI:  https://doi.org/10.1521/pdps.2019.47.1.27
  10. Int J Circumpolar Health. 2019 Dec;78(1): 1578638
    Twenty "must-read" research articles for primary care providers in Nunavik: scoping study and development of an information tool.
    Vincent Paquin, Glenda Sandy, Gentiane Perrault-Sullivan, Gabriel Fortin, Michel Cauchon, Christopher Fletcher, Jean Ouellet, Mélanie Lemire.
      While health needs in Nunavik are distinct, there is a scarcity of knowledge transfer intended for local primary care providers. We aimed to build an information tool in the form of a newsletter and a website to share with them a selection of relevant research articles. To identify such articles, a scoping study of Inuit health research published between 2012 and 2017 was conducted. Selection criteria were adapted from the framework of information mastery. After a database search yielding 2896 results, publications were screened for eligibility. Next, the 226 eligible articles were evaluated and scored for their relevance, their methods (including community participation), their local applicability and their clinical utility. The 20 highest-scored articles were selected for dissemination in a newsletter. They were summarised and presented in 6 thematic emails: Child Development, Infectious Diseases, Traditional and Modern Medicine, Metabolism, Nutrition and Contaminants, and Inuit Perspectives. The newsletter was sent to over 190 health workers and regional stakeholders in Nunavik and was also published online. We hope that this project will foster knowledge sharing and inter-sectorial collaboration between research, public health and clinical care. Trends in Inuit health research are discussed.
    Keywords:  Indigenous health; Inuit health; information management; knowledge transfer; primary care; scoping review
    DOI:  https://doi.org/10.1080/22423982.2019.1578638
This page is being maintained by Thomas Krichel. It was last updated on 2021‒07‒23 at 09:39:01.