bims-librar Biomed News
on Biomedical librarianship
Issue of 2019–06–30
eight papers selected by
Thomas Krichel, Open Library Society
  1. Open collaborative writing with Manubot.
  2. ML-Net: multi-label classification of biomedical texts with deep neural networks.
  3. The Semantic Librarian: A search engine built from vector-space models of semantics.
  4. Collaborative Search Engine for Enhancing Personalized User Search Based on Domain Knowledge.
  5. Tackling the information overload in healthcare.
  6. The medium is not the message: A content analysis of public information about vaping product regulations in Australia.
  7. The National Library of Medicine.
  8. Building the learning health system: Describing an organizational infrastructure to support continuous learning.
  1. PLoS Comput Biol. 2019 Jun 24. 15(6): e1007128
    Open collaborative writing with Manubot.
    Daniel S Himmelstein, Vincent Rubinetti, David R Slochower, Dongbo Hu, Venkat S Malladi, Casey S Greene, Anthony Gitter.
      Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale. We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts. With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes. A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscript's source changes, the rendered outputs are rebuilt and republished to a web page. Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style. Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge.
    DOI:  https://doi.org/10.1371/journal.pcbi.1007128
  2. J Am Med Inform Assoc. 2019 Jun 24. pii: ocz085. [Epub ahead of print]
    ML-Net: multi-label classification of biomedical texts with deep neural networks.
    Jingcheng Du, Qingyu Chen, Yifan Peng, Yang Xiang, Cui Tao, Zhiyong Lu.
       OBJECTIVE: In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts.
    MATERIALS AND METHODS: ML-Net combines a label prediction network with an automated label count prediction mechanism to provide an optimal set of labels. This is accomplished by leveraging both the predicted confidence score of each label and the deep contextual information (modeled by ELMo) in the target document. We evaluate ML-Net on 3 independent corpora in 2 text genres: biomedical literature and clinical notes. For evaluation, we use example-based measures, such as precision, recall, and the F measure. We also compare ML-Net with several competitive machine learning and deep learning baseline models.
    RESULTS: Our benchmarking results show that ML-Net compares favorably to state-of-the-art methods in multi-label classification of biomedical text. ML-Net is also shown to be robust when evaluated on different text genres in biomedicine.
    CONCLUSION: ML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.
    Keywords:  biomedical literacutre; biomedical text; clinical notes; deep neural network; multi-label text classification
    DOI:  https://doi.org/10.1093/jamia/ocz085
  3. Behav Res Methods. 2019 Jun 25.
    The Semantic Librarian: A search engine built from vector-space models of semantics.
    Harinder Aujla, Matthew J C Crump, Matthew T Cook, Randall K Jamieson.
      Psychologists have made substantial progress at developing empirically validated formal expressions of how people perceive, learn, remember, think, and know. In this article, we present an academic search engine for cognitive psychology that leverages computational expressions of human cognition (vector-space models of semantics) to represent and find articles in the psychological record. The method shows how psychological theory can be used to inform and aid the design of psychologically intuitive computer interfaces.
    Keywords:  BEAGLE; Cognitive computing; Computational linguistics; Document representation and retrieval; Search engine
    DOI:  https://doi.org/10.3758/s13428-019-01268-4
  4. J Med Syst. 2019 Jun 23. 43(8): 243
    Collaborative Search Engine for Enhancing Personalized User Search Based on Domain Knowledge.
    Senthilkumar N C, Pradeep Reddy Ch.
      In the fast moving world, users cross over large amount of data for their daily life. Due to the misinterpretation of the context, user cannot retrieve the proper context or failure to retrieve the information. The main aim of this paper is to design and implement a personalized search engine which works based on the domain of the user with the specific constraints suggested by the user. In this paper, the proposed system, build a search engine with web content which get information from the document corpus for the domain through the cloud databases. Web search engine re-ranks the generic results based on a ranking of a context linked with the domain. In this system, collaborative search service helps to improve the relevancy of the search results and to reduce the overtime on bad links and hence caters to customized needs with collaborative feedback using fuzzy decision tree based on fuzzy rules.
    Keywords:  Fuzzy decision tree; Fuzzy rules; Web search engine
    DOI:  https://doi.org/10.1007/s10916-019-1350-1
  5. Br J Nurs. 2019 Jun 27. 28(12): S3
    Tackling the information overload in healthcare.
    Jeanette Milne.
      
    DOI:  https://doi.org/10.12968/bjon.2019.28.12.S3
  6. Drug Alcohol Rev. 2019 Jun 26.
    The medium is not the message: A content analysis of public information about vaping product regulations in Australia.
    Kyle Eu Soon Saw, Kylie Morphett, Cheneal Puljević, Marilyn Bromberg, Coral Gartner.
       INTRODUCTION AND AIMS: Vaping products have been growing in popularity in recent years, including in Australia. Australian laws covering vaping products are complex and vary significantly between jurisdictions. It has been acknowledged that there is public confusion about these laws. This study aims to explore publically-available information about vaping products-related laws disseminated via mainstream media and key stakeholder websites.
    DESIGN AND METHODS: A content analysis was conducted on 302 news articles identified in the Factiva database, and on 73 key stakeholder websites that provided information about vaping product regulations in Australia between January 2005 and January 2018. Items were coded for the type of regulations discussed, the source of information and the information provided about the legal status of vaping products.
    RESULTS: Public advice covered regulations around sales, public use, nicotine importation, nicotine's classification as a poison and nicotine possession. In the majority of news articles, journalists did not cite the source of the information pertaining to vaping products laws, making it difficult for the public to judge its accuracy. We identified several inconsistencies in the information being disseminated through both channels.
    DISCUSSION AND CONCLUSIONS: The inconsistent information provided to the public regarding vaping products likely reflects Australia's complex and varying laws governing the sale, use and possession of vaping products with and without nicotine. We recommend that relevant Australian federal, state and territory health agencies provide a clear and consistent message that covers all relevant information pertaining to vaping products and nicotine within respective jurisdictions.
    Keywords:  e-cigarettes; media; nicotine; regulation; vaping
    DOI:  https://doi.org/10.1111/dar.12961
  7. Am J Nurs. 2019 Jul;119(7): 53-54
    The National Library of Medicine.
    Tracy A Ortelli.
      Free, evidence-based resources for health care providers, patients, and consumers.
    DOI:  https://doi.org/10.1097/01.NAJ.0000569452.97164.81
  8. Learn Health Syst. 2017 Oct;1(4): e10034
    Building the learning health system: Describing an organizational infrastructure to support continuous learning.
    Sally Kraft, William Caplan, Elizabeth Trowbridge, Sarah Davis, Stephanie Berkson, Sandra Kamnetz, Nancy Pandhi.
       Introduction: Academic health centers are reorganizing in response to dramatic changes in the health-care environment. To improve value, they and other health systems must become a learning health system, specifically one that has the capacity to understand performance across the continuum of care and use that information to achieve continuous improvements in efficiency and effectiveness. While learning health system concepts have been well described, the practical steps to create such a system are not well defined. Establishing the necessary infrastructure is particularly challenging at academic health centers due to their tripartite missions and complex organizational structures.
    Methods: Using an evidence-based framework, this article describes a series of organizational-level interventions implemented at an academic health center to create the structures and processes to support the functions of a learning health system.
    Results: Following implementation of changes from 2008 to 2013, system-level performance improved in multiple domains: patient satisfaction, population health screenings, improvement education, and patient engagement.
    Conclusions: This experience can be applied to health systems that wrestle with making system-level change when existing cultures, structures, and processes vary. Using an evidence -based framework is useful when developing the structures and processes that support the functions of a learning health system.
    Keywords:  academic health center; learning health system; quality improvement
    DOI:  https://doi.org/10.1002/lrh2.10034
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