bims-librar Biomed News
on Biomedical librarianship
Issue of 2019‒05‒12
seven papers selected by
Thomas Krichel
Open Library Society

  1. BMJ Open. 2019 May 05. 9(5): e026202
    Qiu Y, Ren W, Liu Y, Yin P, Ren J.
      OBJECTIVE: To determine the prevalence, degree of trust and usefulness of the online health information seeking source and identify associated factors in the adult population from the rural region of China.DESIGN: A cross-sectional population-based study.
    SETTING: A self-designed questionnaire study was conducted between May and June 2015 in four districts of Zhejiang Province.
    PARTICIPANTS: 652 adults aged ≥18 years (response rate: 82.8%).
    PRIMARY OUTCOME MEASURES: The prevalence, degree of trust and usefulness of online health information was the primary outcome. The associated factors were investigated by χ2 test.
    RESULTS: Only 34.8% of participants had faith in online health information; they still tended to select and trust a doctor which is the first choice for sources of health information. 36.7% of participants, being called 'Internet users', indicated that they had ever used the internet during the last 1 year. Among 239 internet users, 40.6% of them reported having sought health information via the internet. And 103 internet users responded that online health information was useful. Inferential analysis demonstrated that younger adults, individuals with higher education, people with a service-based tertiary industry career and excellent health status used online health information more often and had more faith in it (p<0.001).
    CONCLUSIONS: Using the internet to access health information is uncommon in the rural residential adult population in Zhejiang, China. They still tend to seek and trust health information from a doctor. Internet as a source of health information should be encouraged.
    Keywords:  doctor; health information seeking; internet; trust
  2. Sci Data. 2019 May 10. 6(1): 52
    Zhang Y, Chen Q, Yang Z, Lin H, Lu Z.
      Distributed word representations have become an essential foundation for biomedical natural language processing (BioNLP), text mining and information retrieval. Word embeddings are traditionally computed at the word level from a large corpus of unlabeled text, ignoring the information present in the internal structure of words or any information available in domain specific structured resources such as ontologies. However, such information holds potentials for greatly improving the quality of the word representation, as suggested in some recent studies in the general domain. Here we present BioWordVec: an open set of biomedical word vectors/embeddings that combines subword information from unlabeled biomedical text with a widely-used biomedical controlled vocabulary called Medical Subject Headings (MeSH). We assess both the validity and utility of our generated word embeddings over multiple NLP tasks in the biomedical domain. Our benchmarking results demonstrate that our word embeddings can result in significantly improved performance over the previous state of the art in those challenging tasks.
  3. J Orthod. 2019 Mar;46(1): 20-26
    Olkun HK, Demirkaya AA, Aras B.
      OBJECTIVES: This study investigated the quality of Internet information in the English language about lingual orthodontics.DESIGN: A cross-sectional study.
    MATERIALS AND METHODS: An Internet search using the keywords 'lingual orthodontics', 'lingual braces', 'lingual treatment' and 'lingual brackets' was conducted on the four most popular search engines (Google, Yahoo, Bing and AOL) on 4 February 2017. The first 10 websites for each keyword and search engine were screened. After excluding duplicates and irrelevant websites, the remaining were assessed using the DISCERN tool and JAMA benchmarks.
    RESULTS: Of the original 160 websites found, 132 were excluded (102 duplicates, 30 unrelated). The authors of the remaining 28 websites were orthodontists (39.2%), professional organisations (21%), unspecified (17.8%), dentists (7.1%), dental hygienists (7.1%) and patients (7.1%). The mean overall DISCERN score for the 28 websites was poor (36.3). Only 1/28 websites met all four principles of JAMA, four websites met three criteria, 10 websites met two criteria and 13 websites met one criterion.
    CONCLUSION: Online information on lingual orthodontics was of poor quality; moreover, unbiased and balanced information was rare. Orthodontists should be aware that the average quality of information on the Internet about lingual orthodontics might be inadequate and should direct patients to higher-quality websites.
    Keywords:  ; DISCERN; internet; lingual orthodontics; lingual treatment
  4. J Med Internet Res. 2019 Apr 04. 21(5): e10946
    Nitzburg G, Weber I, Yom-Tov E.
      BACKGROUND: Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information.OBJECTIVE: Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information.
    METHODS: We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried.
    RESULTS: In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified.
    CONCLUSIONS: Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness.
    Keywords:  12-step programs; alcohol use disorder; anonymized internet search log data; brief intervention; brief physician advice; substance use disorder
  5. J Biomed Inform. 2019 May 07. pii: S1532-0464(19)30120-0. [Epub ahead of print] 103202
    Olorisade BK, Brereton P, Andras P.
      CONTEXT: Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalanced between the relevant and the irrelevant classes of returned citations. Class imbalance among other factors has been a persistent problem that impairs the performance of TM models, particularly in the context of automatic citation screening for systematic reviews. This has often caused the performance of classification models using the basic title and abstract data to ordinarily fall short of expectations.OBJECTIVE: In this study, we explore the effects of using full bibliography data in addition to title and abstract on text classification performance for automatic citation screening.
    METHODS: We experiment with binary and Word2vec feature representations and SVM models using 4 software engineering (SE) and 15 medical review datasets. We build and compare 3 types of models (binary-non-linear, Word2vec-linear and Word2vec-non-linear kernels) with each dataset using the two feature sets.
    RESULTS: The bibliography enriched data exhibited consistent improved performance in terms of recall, work saved over sampling (WSS) and Matthews correlation coefficient (MCC) in 3 of the 4 SE datasets that are fairly large in size. For the medical datasets, the results vary, however in the majority of cases the performance is the same or better.
    CONCLUSION: Inclusion of the bibliography data provides the potential of improving the performance of the models but to date results are inconclusive.
    Keywords:  Citation screening automation; Computing methodologies; Feature enrichment; Systematic reviews; Text mining
  6. JMIR Hum Factors. 2019 Apr 21. 6(2): e11480
    Hsu WC.
      BACKGROUND: The world's internet penetration rate is increasing yearly; approximately 25% of the world's population are internet users. In Asia, Taiwan has the fifth highest internet usage, and has an internet penetration rate higher than the world average. Electronic health (eHealth) literacy is the ability to read, understand, and utilize Web health information. eHealth literacy is gaining attention worldwide.OBJECTIVE: This study aimed compare the differences in eHealth literacy between traditional college students (aged between 18 and 22 years) and older adult students (aged between 55 and 72 years). It also summarizes the experiences and performances of these 2 groups in terms of searching online health-related information.
    METHODS: A mixed-method approach was used, including questionnaire surveys and interviews. A total of 208 respondents were interviewed: 65 traditional college students (31.3%) and 143 older adult students (68.7%). The results of the interviews were used to compare the eHealth literacy scores of the 2 groups.
    RESULTS: There were significant differences in the overall eHealth literacy scores (t207=2.98; P=.001) and the functional eHealth literacy dimension (t207=12.17; P<.001). The findings showed a significant gap in eHealth literacy between the 2 groups. Most participants believed that online health information could be largely read and understood. However, they were skeptical about the quality of the information and noted that it consisted of either subjective judgments or objective standards.
    CONCLUSIONS: Traditional college students preferred esthetically pleasing health information, whereas older adult students focused on its promotion. Furthermore, the first group often used websites for solving health problems, whereas the second group forwarded health information through communication software.
    Keywords:  eHealth literacy; intergenerational relations; mixed method; older adult students; traditional college students
  7. PLoS One. 2019 ;14(5): e0216126
    Quang MN, Rogers T, Hofman J, Lanham AB.
      AIMS: Enhanced Biological Phosphorus Removal (EBPR) is a technology widely used in wastewater treatment to remove phosphorus (P) and prevent eutrophication. Establishing its operating efficiency and stability is an active research field that has generated almost 3000 publications in the last 40 years. Due to its size, including over 119 review articles, it is an example of a field where it becomes increasingly difficult to manually recognize its key research contributions, especially for non-experts or newcomers. Therefore, this work included two distinct but complementary objectives. First, to assemble for the first time a collection of bibliometric techniques into a framework for automating the article selection process when preparing a literature review (section 2). Second, to demonstrate it by applying it to the field of EBPR, producing a bibliometric analysis and a review of the key findings of EBPR research over time (section 3).FINDINGS: The joint analysis of citation networks, keywords, citation profiles, as well as of specific benchmarks for the identification of highly-cited publications revealed 12 research topics. Their content and evolution could be manually reviewed using a selection of articles consisting of approximately only 5% of the original set of publications. The largest topics addressed the identification of relevant microorganisms, the characterization of their metabolism, including denitrification and the competition between them (Clusters A-D). Emerging and influential topics, as determined by different citation indicators and temporal analysis, were related to volatile fatty acid production, P-recovery from waste activated sludge and aerobic granules for better process efficiency and stability (Clusters F-H).
    CONCLUSIONS: The framework enabled key contributions in each of the constituent topics to be highlighted in a way that may have otherwise been biased by conventional citation-based ranking. Further, it reduced the need for manual input and a priori expertise compared to a traditional literature review. Hence, in an era of accelerated production of information and publications, this work contributed to the way that we are able to use computer-aided approaches to curate information and manage knowledge.