bims-librar Biomed News
on Biomedical librarianship
Issue of 2019‒05‒05
fourteen papers selected by
Thomas Krichel
Open Library Society

  1. Am J Orthod Dentofacial Orthop. 2019 May;pii: S0889-5406(19)30003-4. [Epub ahead of print]155(5): 741-743
    Littlewood A, Kloukos D.
  2. J Clin Epidemiol. 2019 Apr 30. pii: S0895-4356(19)30083-6. [Epub ahead of print]
    Frandsen TF, Eriksen MB, Grøne Hammer DM, Christensen JB.
      OBJECTIVE: PubMed is one of the most commonly used search tools in Biomedical and Life Sciences. Existing studies on database coverage generally conclude that searching PubMed may not be sufficient although some find that the contributions from other databases are modest at best. However, generalizability of the studies of the coverage of PubMed is typically restricted. The objective of this study is to analyze the coverage of PubMed across specialties and over time.STUDY DESIGN AND SETTING: We use the more than 50,000 included studies in all Cochrane reviews published from 2012 to 2016 as our population and examine if the studies as well as resulting publications can be identified in PubMed.
    RESULTS: The results show that PubMed has a coverage of 70.9, 95% CI [68.40,73.30] of all the included publications and 82.8%, 95% CI [80.9, 84.7]. There are huge differences in coverage across as well as within specialties. In addition, coverage varies within groups over time.
    CONCLUSION: Databases used for searching topics within the groups with highly varying or low coverage should be chosen with care as PubMed may have a relatively low coverage.
    Keywords:  Cochrane; PubMed; bibliographic databases; information storage and retrieval; systematic reviews
  3. Database (Oxford). 2019 Jan 01. pii: baz045. [Epub ahead of print]2019
    Jiang X, Ringwald M, Blake JA, Arighi C, Zhang G, Shatkay H.
      Published literature is an important source of knowledge supporting biomedical research. Given the large and increasing number of publications, automated document classification plays an important role in biomedical research. Effective biomedical document classifiers are especially needed for bio-databases, in which the information stems from many thousands of biomedical publications that curators must read in detail and annotate. In addition, biomedical document classification often amounts to identifying a small subset of relevant publications within a much larger collection of available documents. As such, addressing class imbalance is essential to a practical classifier. We present here an effective classification scheme for automatically identifying papers among a large pool of biomedical publications that contain information relevant to a specific topic, which the curators are interested in annotating. The proposed scheme is based on a meta-classification framework using cluster-based under-sampling combined with named-entity recognition and statistical feature selection strategies. We examined the performance of our method over a large imbalanced data set that was originally manually curated by the Jackson Laboratory's Gene Expression Database (GXD). The set consists of more than 90 000 PubMed abstracts, of which about 13 000 documents are labeled as relevant to GXD while the others are not relevant. Our results, 0.72 precision, 0.80 recall and 0.75 f-measure, demonstrate that our proposed classification scheme effectively categorizes such a large data set in the face of data imbalance.
  4. J Med Syst. 2019 May 01. 43(6): 164
    Vijayalakshmi Yellepeddi , Manimegalai P , Sasidhar Babu Suvanam .
      The age of information has done it simple for storing huge amount of data. In actual fact, a considerable segment of existing information is accumulated in the text databases that have huge set of documents from different sources like research articles, news articles, books, e-mail messages, web pages and digital libraries. In many text databases, stored data are in the semi-structured format in that they are neither entirely structured nor entirely unstructured. IR (Information Retrieval) field has been growing in parallel using database systems for several years. Contrasting to the databases system fields that have concentrated mainly on transaction and query processing of the structured data, IR is concerned with firm and retrieval of data from a huge quantity of text-oriented documents. Thus, IR tackles with unstructured and/or semi-structured databases. Information security requirements within a firm have experience major variations in the past some decades. By the establishment of computer, the necessary for automated equipment for securing files as well as other information that stored on the computer turned into evident. This is particularly in case of shared information resources via public network. This is the origin for having a secure computer system / the need for computer security. Computer Security can be achieved by Intrusion Detection Systems. In this paper, we address these issues by applying Similarity Search in two diversified fields: Digital Libraries and Computer Security. The paper discusses a fast and efficient similarity search technique for approximate retrieval of books metadata in Digital Libraries. In DLI the books retrieval takes place just by using metadata such as title, year, edition, author, publishing of a book. Though, if metadata is missing, incorrect or unfinished, then it creates the library retrieval system inefficient, incorrect leads too much confusion to the user. In this context even if the query from the user matches partially or fully with a stored pattern, the information related to that be retrieved. The paper talks about a method that functions rapid and effective, language independent, and flexible library retrieval system signature based similarity search. This system is able to retrieve not only the metadata that exactly matches the query but also fairly accurate identical because of missing words, jumbled words and spell mistakes. Fundamentally, signature file approach is used here. A signature file approach looks like the most capable for huge database as it has superior text retrieval features and requires little storage overhead.
    Keywords:  Information retrieval; Information security; Similarity search
  5. J Public Health Res. 2019 Mar 11. 8(1): 1518
    Diviani N, Fredriksen EH, Meppelink CS, Mullan J, Rich W, Sudmann TT.
      Background: Online health information (OHI) is widely available and consulted by many people in Western countries to gain health advice. The main goal of the present study is to provide a detailed account of the experiences among people from various demographic backgrounds living in high-income countries, who have used OHI.Design and methods: Thematic analysis of 165 qualitative semi-structured interviews conducted among OHI users residing in Australia, Israel, the Netherlands, Norway, and Switzerland was performed.
    Results: The lived experience of people using OHI seem not to differ across countries. The interviews show that searches for OHI are motivated from curiosity, sharing of experiences, or affirmation for actions already taken. Most people find it difficult to appraise the information, leading them to cross-check sources or discuss OHI with others. OHI seems to impact mostly some specific types of health behaviors, such as changes in diet or physical activity, while it only plays a complementary role for more serious health concerns. Participants often check OHI before seeing their GP, but are reluctant to discuss online content with health care personnel due to expected negative reception.
    Conclusions: This study adds to the body of knowledge on eHealth literacy by demonstrating how OHI affects overall health behavior, strengthens patients' ability to understand, live with, and prepare themselves for diverse health challenges. The increasing digitalization of health communication and health care calls for further research on digital divides and patient-professional relations. Health care professionals should acknowledge OHI seeking and engage in discussions with patients to enable them to appreciate OHI, and to support shared decision making in health care. The professionals can utilize patient's desire to learn as a resource for health prevention, promotion or treatment, and empowerment.
    Keywords:  Health communication; Health information seeking; Online health information; Qualitative methods; eHealth literacy
  6. J Med Internet Res. 2019 May 02. 21(5): e12522
    Sun Y, Zhang Y, Gwizdka J, Trace CB.
      BACKGROUND: As the quality of online health information remains questionable, there is a pressing need to understand how consumers evaluate this information. Past reviews identified content-, source-, and individual-related factors that influence consumer judgment in this area. However, systematic knowledge concerning the evaluation process, that is, why and how these factors influence the evaluation behavior, is lacking.OBJECTIVE: This review aims (1) to identify criteria (rules that reflect notions of value and worth) that consumers use to evaluate the quality of online health information and the indicators (properties of information objects to which criteria are applied to form judgments) they use to support the evaluation in order to achieve a better understanding of the process of information quality evaluation and (2) to explicate the relationship between indicators and criteria to provide clear guidelines for designers of consumer health information systems.
    METHODS: A systematic literature search was performed in seven digital reference databases including Medicine, Psychology, Communication, and Library and Information Science to identify empirical studies that report how consumers directly and explicitly describe their evaluation of online health information quality. Thirty-seven articles met the inclusion criteria. A qualitative content analysis was performed to identify quality evaluation criteria, indicators, and their relationships.
    RESULTS: We identified 25 criteria and 165 indicators. The most widely reported criteria used by consumers were trustworthiness, expertise, and objectivity. The indicators were related to source, content, and design. Among them, 114 were positive indicators (entailing positive quality judgments), 35 were negative indicators (entailing negative judgments), and 16 indicators had both positive and negative quality influence, depending on contextual factors (eg, source and individual differences) and criteria applied. The most widely reported indicators were site owners/sponsors; consensus among multiple sources; characteristics of writing and language; advertisements; content authorship; and interface design.
    CONCLUSIONS: Consumer evaluation of online health information is a complex cost-benefit analysis process that involves the use of a wide range of criteria and a much wider range of quality indicators. There are commonalities in the use of criteria across user groups and source types, but the differences are hard to ignore. Evidently, consumers' health information evaluation can be characterized as highly subjective and contextualized, and sometimes, misinformed. These findings invite more research into how different user groups evaluate different types of online sources and a personalized approach to educate users about evaluating online health information quality.
    Keywords:  consumer health informatics; health information quality; health information seeking; online health information
  7. Telemed J E Health. 2019 May 01.
    Zhang H, Zhang R, Lu X, Zhu X.
      Background: Personal trust tendency is an individual characteristic that can affect one's evaluation of others, behavior and its related outcomes. It may significantly affect one's health information seeking behavior and compliance. Therefore, this article aims at figuring out how personal trust tendency influences patient compliance through the internet health information seeking and patient satisfaction with it. Methods: Data were collected from 336 valid participants through an online survey in China. There are two independent variables: (1) cognition-based trust tendency and (2) affect-based trust tendency, three intervening variables (emerging internet health information seeking, conservative internet health information seeking, and satisfaction with internet health information), one dependent variable (patient compliance), and control variables. We performed confirmative factor analysis and structural equation modeling to test the hypotheses. Results: The cognition- and affect-based trust tendency positively affects patient compliance through the mediation of emerging and conservative internet health information seeking and satisfaction with internet health information. Surprisingly, strong positive relationships between affect-based trust tendency and emerging and conservative internet health information seeking were found, which are contrary to our initial hypothesis. Conclusions: Health information is considerably important when regarding health-related issues for individuals with cognition- and affect-based trust tendency. Physicians should encourage patients to seek health information on the internet and guide them to use internet health information that suits them. Information exchange and correlations should be involved in doctor-patient interactions. By following the suggestions just cited, better patient compliance can likely be obtained.
  8. Clin Pediatr (Phila). 2019 May 01. 9922819845163
    Sood N, Jimenez DE, Pham TB, Cordrey K, Awadalla N, Milanaik R.
      This study investigates how parental trust in physician diagnoses and likelihood of seeking a second opinion (SO) are affected by Internet sources. In an anonymous survey, 1374 parents of minors viewed a vignette describing their child's symptoms followed by Internet results that either supported or contradicted the pediatrician's diagnosis (Dx). A control group did not view any Internet results. After learning the Dx, participants rated trust in the Dx and likelihood of seeking a SO on a 7-point Likert-type scale. Participants who viewed contradicting results were less likely to trust the Dx ( P < .001) and more likely to seek a SO than the control ( P < .001). Participants who viewed supporting results were more likely to trust the Dx ( P < .001) and less likely to seek a SO than the control ( P < .001). Physicians must be aware of the influence the Internet may have on patients' trust.
    Keywords:  Internet; diagnosis; differential diagnosis; online health information; pediatrician; search results; second opinion; trust
  9. Int J Med Inform. 2019 Jun;pii: S1386-5056(18)30830-X. [Epub ahead of print]126 35-45
    Oliveira JL, Trifan A, Bastião Silva LA.
      OBJECTIVE: The collaboration and knowledge exchange between researchers are often hindered by the nonexistence of accurate information about which databases may support research studies. Even though a considerable amount of patient health information does exist, it is usually distributed and hidden in many institutions. The goal of this project is to provide, for any research community, a holistic view of biomedical datasets of interests, from which researchers can explore several distinct levels of granularity.METHODS: We developed a community-centered approach to facilitate data sharing while ensuring privacy. A dynamic schema allows exposing any metadata model about existing repositories. The framework was developed following a modular plugin-based architecture that facilitates the integration of internal and external tools.
    RESULTS: The EMIF Catalogue, a web platform for sharing and reusing biomedical data. Through this system, data custodians can publish and share different levels of information, while the researchers can search for databases that fulfill research requirements.
    CONCLUSIONS: The EMIF Catalogue currently fosters several distinct research communities, with different levels of data governance, combining, for instance, data available in pan-European EHR and Alzheimer cohorts. This portal is publicly available at
    Keywords:  Biomedical data integration; Data catalogue; Data discovery; Data reuse; Data sharing; Research study
  10. BMC Med Inform Decis Mak. 2019 Apr 27. 19(1): 93
    Müller L, Gangadharaiah R, Klein SC, Perry J, Bernstein G, Nurkse D, Wailes D, Graham R, El-Kareh R, Mehta S, Vinterbo SA, Aronoff-Spencer E.
      INTRODUCTION: While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases.METHODS: We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material.
    RESULTS: The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as "best suggestion" in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems.
    CONCLUSION: We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.
    Keywords:  Decision support systems, clinical (D020000); Diagnosis, differential (D003937); Knowledge bases (D051188)
  11. Front Psychiatry. 2019 ;10 188
    Hazewinkel MC, de Winter RFP, van Est RW, van Hyfte D, Wijnschenk D, Miedema N, Hoencamp E.
      Aim: With the introduction of "Electronic Medical Record" (EMR) a wealth of digital data has become available. This provides a unique opportunity for exploring precedents for seclusion. This study explored the feasibility of text mining analysis in the EMR to eventually help reduce the use of seclusion in psychiatry. Methods: The texts in notes and reports of the EMR during 5 years on an acute and non-acute psychiatric ward were analyzed using a text mining application. A period of 14 days was selected before seclusion or for non-secluded patients, before discharge. The resulting concepts were analyzed using chi-square tests to assess which concepts had a significant higher or lower frequency than expected in the "seclusion" and "non-seclusion" categories. Results: Text mining led to an overview of 1,500 meaningful concepts. In the 14 day period prior to the event, 115 of these concepts had a significantly higher frequency in the seclusion category and 49 in the non-seclusion category. Analysis of the concepts from days 14 to 7 resulted in 54 concepts with a significantly higher frequency in the seclusion-category and 14 in the non-seclusion category. Conclusions: The resulting significant concepts are comparable to reasons for seclusion in literature. These results are "proof of concept". Analyzing text of reports in the EMR seems therefore promising as contribution to tools available for the prediction of seclusion. The next step is to build, train and test a model, before text mining can be part of an evidence-based clinical decision making tool.
    Keywords:  data mining; electronic medical record; psychiatric inpatient ward; seclusion; text mining
  12. J Health Commun. 2019 Apr 29. 1-9
    Park M.
      A large number of posts promoting risky health behavior are posted on social media, but not all posts are widely disseminated. Disseminated information is more likely to influence social media users as users may not be exposed to non-disseminated posts. Thus, this study focuses on principal characteristics of disseminated Twitter posts that attract individuals who promote smoking behavior. After collecting 6,432 tweets and analyzing highly disseminated and non-disseminated Tweets, this study found that Tweets expressing emotional support were observed more than expected among the pro-smoking group and less than expected among the anti-smoking group. Affective tone was also found to be an important factor in Tweet dissemination both in pro- and anti-smoking groups. Interestingly, in the pro-smoking group, Tweets having a negative tone were disseminated more than those having a positive tone. This paper concludes with the discussion of theoretical and practical implications of the current findings.
  13. Database (Oxford). 2019 Jan 01. pii: baz057. [Epub ahead of print]2019
    Sharma S, Ciufo S, Starchenko E, Darji D, Chlumsky L, Karsch-Mizrachi I, Schoch CL.