bims-librar Biomed News
on Biomedical librarianship
Issue of 2020‒12‒20
seventeen papers selected by
Thomas Krichel
Open Library Society

  1. Database (Oxford). 2019 Jan 01. pii: baz085. [Epub ahead of print]2019
      Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
  2. BMC Med Inform Decis Mak. 2020 Dec 14. 20(Suppl 4): 314
      BACKGROUND: Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer's Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society.METHODS: In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer's Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool.
    RESULTS: The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation.
    CONCLUSIONS: We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.
    Keywords:  Information Extraction; Knowledge Graphs; Ontologies; RDF Triples
  3. Syst Rev. 2020 Dec 13. 9(1): 293
      BACKGROUND: Despite existing research on text mining and machine learning for title and abstract screening, the role of machine learning within systematic literature reviews (SLRs) for health technology assessment (HTA) remains unclear given lack of extensive testing and of guidance from HTA agencies. We sought to address two knowledge gaps: to extend ML algorithms to provide a reason for exclusion-to align with current practices-and to determine optimal parameter settings for feature-set generation and ML algorithms.METHODS: We used abstract and full-text selection data from five large SLRs (n = 3089 to 12,769 abstracts) across a variety of disease areas. Each SLR was split into training and test sets. We developed a multi-step algorithm to categorize each citation into the following categories: included; excluded for each PICOS criterion; or unclassified. We used a bag-of-words approach for feature-set generation and compared machine learning algorithms using support vector machines (SVMs), naïve Bayes (NB), and bagged classification and regression trees (CART) for classification. We also compared alternative training set strategies: using full data versus downsampling (i.e., reducing excludes to balance includes/excludes because machine learning algorithms perform better with balanced data), and using inclusion/exclusion decisions from abstract versus full-text screening. Performance comparisons were in terms of specificity, sensitivity, accuracy, and matching the reason for exclusion.
    RESULTS: The best-fitting model (optimized sensitivity and specificity) was based on the SVM algorithm using training data based on full-text decisions, downsampling, and excluding words occurring fewer than five times. The sensitivity and specificity of this model ranged from 94 to 100%, and 54 to 89%, respectively, across the five SLRs. On average, 75% of excluded citations were excluded with a reason and 83% of these citations matched the reviewers' original reason for exclusion. Sensitivity significantly improved when both downsampling and abstract decisions were used.
    CONCLUSIONS: ML algorithms can improve the efficiency of the SLR process and the proposed algorithms could reduce the workload of a second reviewer by identifying exclusions with a relevant PICOS reason, thus aligning with HTA guidance. Downsampling can be used to improve study selection, and improvements using full-text exclusions have implications for a learn-as-you-go approach.
    Keywords:  Classification; Downsampling; Machine learning; Methods; Reasons for exclusion; Study selection; Systematic literature reviews; Text mining; Updates
  4. ANS Adv Nurs Sci. 2020 Dec 10.
      The quality of literature used as the foundation to any research or scholarly project is critical. The purpose of this study was to analyze the extent to which predatory nursing journals were included in credible databases, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Scopus, commonly used by nurse scholars when searching for information. Findings indicated that no predatory nursing journals were currently indexed in MEDLINE or CINAHL, and only one journal was in Scopus. Citations to articles published in predatory nursing journals are not likely found in a search using these curated databases but rather through Google or Google Scholar search engines.
  5. Health Info Libr J. 2020 Dec 17.
      The paper highlights the experience of working with Shane Godbolt when she was a practising Medical librarian, her mentorship to younger librarians/information professionals, her personal and professional support to African librarians and the role she played as the Director of Partnerships in Health Information (Phi). These activities led to successful collaboration between Phi and African librarians. Indeed Shane widened the networks and opened up opportunities for many.
    Keywords:  Africa; East; collaboration; continuing professional development; e; information science; information skills; librarians; librarianship, health science; libraries
  6. Health Info Libr J. 2020 Dec 12.
      This paper introduces Shane's early life up to the point of her joining St Bartholomew's Medical College, London, as an Assistant Librarian. It tracks her links with her professional association and concludes with a cameo of her social life and contribution to her church.
    Keywords:  health science; librarians; librarianship; mentoring; professional associations
  7. Health Info Libr J. 2020 Dec 19.
      This paper describes the significant roles Shane Godbolt played in promoting partnerships and collaborations and strengthening the Association for Health Information and Libraries in Africa (AHILA). It presents the personal reflections of each author about Shane, the part she played in their professional and personal lives as well as Shane's vital support for AHILA and AHILA members during her lifetime.
    Keywords:  Africa; East; International; North; South; West; collaboration; continuing professional development; health sciences; librarianship; professional associations
  8. Health Info Libr J. 2020 Dec 16.
      This paper focuses on Shane Godbolt's commitment to international librarianship and global health and her guiding principles for international working. The authors examine and celebrate how these have been applied in practice, impacting many.
    Keywords:  Access to information; Advocacy; Africa; Developing economies; Global health; Godbolt; Health science; Shane
  9. BMC Bioinformatics. 2020 Dec 17. 21(1): 582
      BACKGROUND: Biomedical research projects deal with data management requirements from multiple sources like funding agencies' guidelines, publisher policies, discipline best practices, and their own users' needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability.RESULTS: We introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform.
    CONCLUSIONS: Management of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects.
    Keywords:  Data management; Drupal; FAIR; Linked data; Metadata; Open source; Persistent identifiers; Research data management; Software
  10. BMC Med Inform Decis Mak. 2020 Dec 15. 20(Suppl 14): 306
      BACKGROUND: Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text.METHODS: A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit.
    RESULTS: The random forests model classified sentences as "good" or "bad" with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care.
    CONCLUSIONS: We have developed a web-based summarization and visualization tool, CERC ( ), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.
    Keywords:  Automatic summarization; Automatic translation; Clinical decision support; Content extraction and recognition; Extracting salient information; Extractive summarization; Indicative summarization; Machine learning; Multi indicator text summarization algorithm; Multiple indicators; Sentence extraction and ranking
  11. Int J Obstet Anesth. 2020 Nov 16. pii: S0959-289X(20)30145-X. [Epub ahead of print]
      BACKGROUND: Large gaps remain in our understanding of the role of social media platforms in the dissemination of medical information. This cross-sectional study quantitatively assessed the accuracy and quality of information on YouTube regarding epidural labor analgesia.METHODS: YouTube was searched on May 23, 2020 using keywords 'epidural,' 'epidural for labor,' 'epidural for pregnancy,' 'epidural experience,' and 'epidural risks,' and the top 50 viewed videos from each search were screened. Primary outcomes included the proportion of videos containing non-factual information, and video quality analyzed using the modified DISCERN (mDISCERN) score.
    RESULTS: Thirteen of 60 (21.7%) videos included non-factual information about epidural analgesia; these videos were viewed more than 16.5 million times (60% of total viewership of the videos analyzed). Mean (standard deviation) mDISCERN score for all included videos was 1.9 (1.3), which is below the threshold for high video-quality. Videos from medical sources (hospitals, medical practices, physicians, other medical professionals) had a higher mDISCERN score compared with videos by non-medical sources (P <0.001). Educational videos from professional societies of obstetrics or obstetric anesthesiology were not captured.
    CONCLUSION: YouTube is an accessible platform for medical information on epidural labor analgesia, although a significant proportion of videos studied contained non-factual information and presented low video quality. Increased efforts by reputable sources including hospitals, physicians, other medical professionals, and professional societies, to disseminate accurate information, are warranted.
    Keywords:  Anesthesia; Communication; Epidural; Information dissemination; Internet; Social media
  12. J Fluency Disord. 2020 Dec 08. pii: S0094-730X(20)30079-6. [Epub ahead of print]67 105824
      PURPOSE: We examined the quality and readability of English-language Internet information about stuttering and evaluated the results considering recommendations by experts in health literacy.METHOD: A search of Internet websites containing information about stuttering was conducted. Three key words (i.e., stuttering, stammering, speech disfluency) were entered into five country-specific versions of the most commonly used Internet search engine. A total of 79 websites were assessed. Their origin (commercial, non-profit, government, personal or university), quality [Health On the Net (HON) certification and DISCERN scores], and readability [Flesch Reading Ease (FRE) score, Flesch-Kincaid Grade Level Formula (F-KGL), and Simple Measure of Gobbledygook (SMOG)] were assessed.
    RESULTS: Of the 79 websites, 38 % were of commercial, 42 % were of nonprofit organization, 15 % were of government and 5% were of university origins, respectively. Only 13 % had obtained HON certification and the mean DISCERN scores was 3.10 in a 5-point scale. The mean reading grade levels were at 13th and 14th grade and 100 % of the websites exceeded the recommended 5th to 6th reading grade level for health information.
    CONCLUSIONS: The quality of Internet-based health information about the treatment of stuttering is generally adequate, but actual usability of the sites examined in this study may be limited due to poor readability levels. This is problematic in persons with poor literacy skills. Since the Internet can be readily accessed as a valuable consumer information resource, speech-language pathologists and other healthcare professionals have an opportunity to direct consumers to websites that provide readable information of good quality.
    Keywords:  Dysfluency; Health information quality; Health information readability; Internet health information; Stammering; Stuttering
  13. F1000Res. 2019 ;8 416
      Background: Patients frequently consult the internet for health information. Our aim was to perform an Internet-based readability and quality control study using recognised quality scoring systems to assess the patient information available online relating to anaesthesia for total hip and knee replacement surgery. Methods: Online patient information relating to anaesthesia for total hip and knee replacement was identified using Google, Bing and Yahoo with search terms 'hip replacement anaesthetic', 'knee replacement anaesthetic.' Readability was assessed using Flesch Reading Ease (FRE), Flesch-Kincaid grade level (FKGL) and Gunning Fog Index (GFI). Quality was assessed using DISCERN instrument, Health On the Net Foundation seal, and Information Standard mark. Results: 32 websites were analysed. 25% were HONcode certified, 15.6% had the Information Standard. Mean FRE was 55.2±12.8. Mean FKGL was 8.6±1.9. Six websites (18.8%) had the recommended 6 th-grade readability level. Mean of 10.4±2.6 years of formal education was required to read the websites. Websites with Information Standard were easier to read: FKGL (6.2 vs. 9, P < 0.001), GFI (8.8 vs. 10.7, P = 0.04), FRE score (64.2 vs. 9, P = 0.02). Mean DISCERN score was low: 40.3 ± 13. Conclusions: Overall, most websites were poor quality with reading levels too high for the target audience. Information Standard NHS quality mark was associated with improved readability, however along with HONcode were not found to have a statistically significant correlation with quality.  Based on this study, we would encourage healthcare professionals to be judicious in the websites they recommend to patients, and to consider both the readability and quality of the information provided.
    Keywords:  Anaesthesia; Internet; Patient information.; Quality; Readability; Total hip replacement; Total knee replacement
  14. J Neurosurg Sci. 2020 Dec 15.
      BACKGROUND: Sciatica is a common neurological condition with a wide variety of clinical specialists and allied health professionals involved, and a broad range of treatment options. We sought to assess the quality of information available on the internet.METHODS: An internet search for 'sciatica' was performed using 'Google'. The first fifty links were assessed using the DISCERN instrument, a validated questionnaire for health consumers and providers.
    RESULTS: After exclusions, 44 websites were assessed. Only 37% of sites had clear aims and objectives; 79% provided relevant information; 81% did not provide clear sources of their information; 67% had no indication of when the information was compiled or updated; 63% clarified that more than one treatment option was available; only 28% described in moderate to extensive detail how the various treatment modalities might work; only 14% informed patients of potential risks and complications for each treatment. The biased and/or unbalanced websites amounted to 40%, offering greater detail about one treatment modality over others. Overall, 93% of assessed websites did not inform patients of the consequences/natural history if no treatment were undertaken; and 91% did not describe the potential impact of treatment and how it could affect quality of life.
    CONCLUSIONS: Despite the role that the internet plays in everyday life, information on the common and debilitating condition of sciatica is mostly of low-to-moderate quality, and with serious shortcomings. Healthcare stakeholders ought to be aware of the risks of misinformation and ensure that health-related internet website design and upkeep is guided by instruments such as DISCERN.
  15. J Nurs Scholarsh. 2020 Dec 14.
      PURPOSE: A deluge of fake news and misinformation about the coronavirus disease 2019 (COVID-19) on the Internet poses challenges for the public in their search for reliable and relevant health information for taking protective measures, especially among people with chronic diseases (PWCD). This study aimed to (a) understand the satisfaction level of the online information related to COVID-19 in people with and without chronic diseases; (b) explore information-searching behavior and digital health literacy in PWCD; and (3) identify the possible predictors of information satisfaction among PWCD.METHODS: This was a multicity, cross-sectional study using an online survey with a convenience sample of people who (a) were 15 years of age or older and (b) had access to the Internet in mainland China, Hong Kong, and Macau.
    FINDINGS: Four thousand four hundred and seventy-two subjects completed the survey, of whom less than 50% felt satisfied with the online information. About 20% of respondents (n = 882) were diagnosed with at least one chronic disease and reported a lower level of information satisfaction (p = .003) than the people without chronic diseases. The majority of the PWCD obtained their online health information from social media. Higher digital health literacy (adjusted odds ratio [OR] = 5.07), higher frequency of searches regarding symptoms of COVID-19 (adjusted OR = 2.07), higher perceived importance of quickly learning from the information searched (adjusted OR = 1.63), and lower frequency of searches on the topic of dealing with psychological stress (adjusted OR = 0.54) were found to be predictors of information satisfaction among PWCD.
    CONCLUSIONS: The majority of PWCD sought online information related to COVID-19 from social media, and their level of information satisfaction was significantly lower than among people without chronic diseases. Digital health literacy is a strong and significant predictor of information satisfaction.
    CLINICAL RELEVANCE: To support PWCD, we not only have to provide them with clear and accurate information, but also promote their digital health literacy so that they may seek, understand, and appraise health information from the Internet to make appropriate health-related judgments and decisions.
    Keywords:  Chronic diseases; global health; health promotion; informatics; public health
  16. Int J Environ Res Public Health. 2020 Dec 15. pii: E9386. [Epub ahead of print]17(24):
      Social campaigns are carried out to promote autism spectrum disorder (ASD) awareness, normalization, and visibility. The internet helps to shape perceptions of Asperger syndrome and autism. In fact, these campaigns often coincide with the increase in searches for both diagnoses on Google. We have two study objectives: to use Google Trends to identify the annual time points from 2015 to 2019 with the highest Google search traffic in Spain for the terms "autism" and "Asperger", and to identify news and trending topics related to ASD that took place during the weeks with the highest number of Google searches for these terms. Google Trend, MyNews and Trendinalia were used to analyze the volume of searches and trending topics related to ASD. As a result, social marketing campaigns, social networks and the publication of news items act as powerful voices that can provide a realistic or sensationalist picture of the disorder. For this reason, we concluded that campaigns play an important role in the normalization of ASD, and that it is important for organizations concerned with the visibility and social inclusion of people with ASD to check the way ASD is portrayed through the internet, media, and social networks.
    Keywords:  Asperger; Google trends; MyNews; Trendinalia; autism; internet users’ behavior; social campaigns