bims-librar 2019-05-26 papers

bims-librar

Biomed News

on Biomedical librarianship

Issue of 2019‒05‒26
eight papers selected by
Thomas Krichel
Open Library Society

Assessing Electronic Health Literacy in the State of Kuwait: Survey of Internet Users From an Arab State.
Patterns of internet and social media use in colorectal surgery.
PubTator central: automated concept annotation for biomedical full text articles.
Quantitative Readability Assessment of the Internal Medicine Online Patient Information on Annals.org.
The Influence of Offline and Online Intrinsic Motivations on Online Health Information Seeking.
The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study.
Document/Query Expansion based on Selecting Significant Concepts for Context Based Retrieval of Medical Images.
Trust in national health information sources in the United States: comparing predictors and levels of trust across three health domains.

J Med Internet Res. 2019 May 24. 21(5): e11174

Assessing Electronic Health Literacy in the State of Kuwait: Survey of Internet Users From an Arab State.

Dari Alhuwail, Yousef Abdulsalam.

  BACKGROUND: The internet and social media have become an important source for health information. In 2017, the State of Kuwait ranked first in mobile subscription penetration in the Arab world; nearly 90% of its population uses the internet. Electronic health (eHealth) literacy is important in populations that have easy and affordable access to internet resources to more effectively manage health conditions as well as improve general population health.OBJECTIVE: The aim of this study was to assess eHealth literacy levels across internet users in Kuwait and identify demographic characteristics that influence eHealth literacy. Furthermore, the study aimed to identify the reasons and type of information that people seek online. Finally, this study examined the utilization of various social media channels for accessing online health information. The social media platforms considered were as follows: WhatsApp, Twitter, Instagram, YouTube, Facebook, and Snapchat.
METHODS: A cross-sectional anonymous Web-based survey was used to collect data about eHealth literacy and related information. The eHealth literacy scale (eHEALS), originally developed by Norman and Skinner, is measured using 8 Likert-type scales. A linear regression model estimates the effect of demographic variables such as age, gender, and education on eHealth literacy while controlling for participants' perceived usefulness and importance of the internet. Participants were also surveyed about their frequency in using social media platforms for seeking health information.
RESULTS: Kuwait's composite eHEALS, based on a sample of 386 participants, was 28.63, which is very similar to eHEALS observed among adult populations in other developed countries. Females in Kuwait demonstrated a higher average eHEALS compared with males. Among the social media platforms, the survey results indicated that YouTube is the most frequently used to seek health information, with Facebook being the least frequently used.
CONCLUSIONS: Internet users in Kuwait appear confident in their ability to search for health-related information online compared with other populations, as indicated by aggregate eHEALS scores. Considering this finding, government and health care organizations should shift more efforts from traditional media toward online health information, focusing on the social media outlets that people in Kuwait find more useful for seeking health information.

Keywords:  Arab; Kuwait; eHEALS; health information; informatics; information-seeking; literacy

DOI:  https://doi.org/10.2196/11174
BMC Surg. 2019 May 24. 19(1): 52

Patterns of internet and social media use in colorectal surgery.

Leonora E Long, Christopher Leung, Jonathan S Hong, Caroline Wright, Christopher J Young.

  BACKGROUND: Surgeons use the Internet and social media to provide health information, promote their clinical practice, network with clinicians and researchers, and engage with journal clubs and online campaigns. While surgical patients are increasingly Internet-literate, the prevalence and purpose of searching for online health information vary among patient populations. We aimed to characterise patient and colorectal surgeon (CRS) use of the Internet and social media to seek health information.METHODS: Members of the Colorectal Society of Australia and New Zealand and patients under the care of CRS at the Royal Prince Alfred Hospital, Sydney, were surveyed. Questions pertained to the types of information sought from the Internet, the platforms used to seek it, and the perceived utility of this information.
RESULTS: Most CRS spent 2-6 h per week using the Internet for clinical purposes and an additional 2-6 h per week for research. 79% preferred literature databases as an information source. CRS most commonly directed patients to professional healthcare body websites. 59% of CRS use social media, mainly for socialising or networking. Nine percent of surgeons spent > 1 h per week on social media for clinical or research purposes. 72% of surgeons have a surgical practice website. 43% of patients searched the Internet for information on their doctor, and 75% of patients sought information on their symptoms or condition. However, 25% used health-specific websites, and 14% used professional healthcare body websites. Around 84% of patients found the information helpful, and 8% found it difficult to find information on the Internet. 12% of patients used social media to seek health information.
CONCLUSIONS: Colorectal surgery patients commonly find health information on the Internet but social media is not a prominent source of health information for patients or CRS.

Keywords:  Australia; Colorectal surgery; Consumer health information; Internet; New Zealand; Social media; Surgeons

DOI:  https://doi.org/10.1186/s12893-019-0518-4
Nucleic Acids Res. 2019 May 22. pii: gkz389. [Epub ahead of print]

PubTator central: automated concept annotation for biomedical full text articles.

Chih-Hsuan Wei, Alexis Allot, Robert Leaman, Zhiyong Lu.

PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.

DOI: https://doi.org/10.1093/nar/gkz389
Cureus. 2019 Mar 06. 11(3): e4184

Quantitative Readability Assessment of the Internal Medicine Online Patient Information on Annals.org.

Ahmad A Abu-Heija, Maya Shatta, Mustafa Ajam, Usama Abu-Heija, Nashat Imran, Diane Levine.

  Background Approximately 90% of Americans have access to the internet with the majority of people searching online for medical information pertaining to their health, or the health of loved ones. The public relies immensely on online health information to make decisions related to their healthcare. The American Medical Association (AMA) and the National Institute of Health (NIH) recommend that publicly available health-related information be written at the level of the sixth-seventh grade. Materials and methods Patient education materials available to the public on the Annals.org, a website sponsored by the American College of Physicians, were collected. All 89 patient education articles were downloaded from the website and analyzed for their ease of readability. The articles were analyzed utilizing a readability software generating five quantitative readability scores: Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG). All scores, with the exception of FRE, generate a grade level that correlates with the required school-grade level to ensure adequate readability of the information. Results Eighty-nine articles were analyzed generating an average score as follows: FRE 62.8, FKGL 7.0, GFI 8.6, CLI 9.6 and SMOG 9.8. Overall, 87.6% of the articles were written at a level higher than the 7th-grade level, which is recommended by the AMA and NIH. Conclusion In an era of increased reliance on the internet for medical information pertaining to patients' health, materials written at a higher grade than recommended has the potential to negatively impact patients' well-being, in addition to tremendous ramifications on the healthcare system. Potentially redrafting, these articles can prove beneficial to patients who rely on these resources for making healthcare-related decisions.

Keywords:  comprehension; health education; health resources; patient education; readability

DOI:  https://doi.org/10.7759/cureus.4184
Health Commun. 2019 May 23. 1-8

The Influence of Offline and Online Intrinsic Motivations on Online Health Information Seeking.

Seow Ting Lee, Julian Lin.

We examine how intrinsic motivations in two health interaction contexts - online and doctor's office visit - influence online health information seeking (OHIS). Many studies have approached OHIS through short-term gratification of informational needs. Our study uses a conceptual framework of intrinsic human motivation to better understand OHIS as a form of sustained behavior. We applied Self Determination Theory's three key constructs (Autonomy, Competence, and Relatedness) within a locus of patient-physician relations. Our findings, based on a survey of 993 online health information seekers in India, show that support for Autonomy in the online context explains all three categories of OHIS behaviors: Diagnosis and Treatment Information Seeking, General Health Information Seeking, and Office Visit Information Seeking. Support for Relatedness in the online context explains only Office Visit Information Seeking. However, support for Autonomy, Competence, and Relatedness in the office visit experience could not explain why people engage in OHIS overall. Motivations for the office visit are not associated with the online experience, suggesting that online and offline are not just two kinds of substitute health interaction contexts.

DOI: https://doi.org/10.1080/10410236.2019.1620088
J Med Internet Res. 2019 May 24. 21(5): e12957

The Deep Learning-Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study.

Xiaoyue Feng, Hao Zhang, Yijie Ren, Penghui Shang, Yi Zhu, Yanchun Liang, Renchu Guan, Dong Xu.

  BACKGROUND: It is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the most suitable publication venue, given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music, and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature.OBJECTIVE: We aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper's abstract.
METHODS: In Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals.
RESULTS: We collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering-based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/.
CONCLUSIONS: Our deep learning-based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

Keywords:  PubMed; biomedical literature; convolutional neural network; deep learning; recommender system

DOI:  https://doi.org/10.2196/12957
J Biomed Inform. 2019 May 17. pii: S1532-0464(19)30128-5. [Epub ahead of print] 103210

Document/Query Expansion based on Selecting Significant Concepts for Context Based Retrieval of Medical Images.

Mouna Torjmen-Khemakhem, Karim Gasmi.

  In the medical image retrieval literature, there are two main approaches: content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with the images. We present a work that fits in the context-based category, where queries are composed of medical keywords and the documents are metadata that succinctly describe the medical images. A main difference between the context-based image retrieval approach and the textual document retrieval is that in image retrieval the narrative description is very brief and typically cannot describe the entire image content, thereby negatively affecting the retrieval quality. One of the solutions offered in the literature is to add new relevant terms to both the query and the documents using expansion techniques. Nevertheless, the use of native terms to retrieve images has several disadvantages such as term-ambiguities. In fact, several studies have proved that mapping text to concepts can improve the semantic representation of the textual information. However, the use of concepts in the retrieval process has its own problems such as erroneous semantic relations between concepts in the semantic resource. In this paper, we propose a new expansion method for medical text (query/document) based on retro-semantic mapping between textual terms and UMLS concepts that are relevant in medical image retrieval. More precisely, we propose mapping the medical text of queries and documents into concepts and then applying a concept-selection method to keep only the most significant concepts. In this way, the most representative term (preferred name) identified in the UMLS for each selected concept is added to the initial text. Experiments carried out with ImageCLEF 2009 and 2010 datasets showed that the proposed approach significantly improves the retrieval accuracy and outperforms the approaches offered in the literature.

Keywords:  Medical images; UMLS; concepts; expansion; information retrieval; preferred name

DOI:  https://doi.org/10.1016/j.jbi.2019.103210
Transl Behav Med. 2019 May 22. pii: ibz066. [Epub ahead of print]

Trust in national health information sources in the United States: comparing predictors and levels of trust across three health domains.

Emily B Peterson, Wen-Ying Sylvia Chou, Dannielle E Kelley, Brad Hesse.

  Public trust in traditional sources of health information is essential for public health agencies and organizations to perform necessary public health functions. Little research has examined levels and predictors of trust in government health agencies and national health organizations. Additionally, few studies have simultaneously analyzed trust in multiple health topics. The major aim of this study was to compare levels and factors associated with trust in national health sources across three health topics: information about tobacco, electronic cigarettes, and general health. Data from two cycles of the National Cancer Institute's Health Information National Trends Survey collected in 2015 and 2017 were merged and analyzed for this study (n = 5,474). A series of weighted multivariable logistic regression models calculated odds of high trust in government health agencies and health organizations for each health topic. More respondents reported high trust in health organizations than for government health agencies across all topics. More participants reported high trust in these sources tobacco information, as compared to general health or e-cigarette information. Logistic models found that those higher in information seeking confidence were more likely to report high trust across all models. Other demographic variables were inconsistent predictors of trust across topics. This study highlights inconsistent sociodemographic predictors of trust across multiple health topics and national health sources. Researchers, practitioners, and policymakers should consider the unique context of specific health topics in health promotion campaigns, partner with existing community-based organizations, and encourage and enable health information seeking.

Keywords:  Confidence in health information seeking; National health information sources; Tobacco

DOI:  https://doi.org/10.1093/tbm/ibz066