Dataset Information

Quantifying the impact and extent of undocumented biomedical synonymy.

ABSTRACT: Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through "crowd-sourcing." Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for "next-generation," high-coverage lexical terminologies.

SUBMITTER: Blair DR

PROVIDER: S-EPMC4177665 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Quantifying the impact and extent of undocumented biomedical synonymy.

Blair David R DR Wang Kanix K Nestorov Svetlozar S Evans James A JA Rzhetsky Andrey A

PLoS computational biology 20140925 9

Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendiu ...[more]

PMID: 25255227

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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts. Population characteristics strongly related to United States (US) county-level data on COVID-19 cases and deaths during all stages of the pandemic were identified from the onset of the epidemic and included county-level socio-demographic and comorbidities data, as well as daily meteorological modeled observation data from the North American Regional Reanalysis (NARR), and the NARR high spatial resolution model to assess the environment. Advanced machine learning (ML) approaches were used to identify outbreaks (geographic clusters of COVID-19) and included spatiotemporal risk factors and COVID-19 vaccination efforts, especially among vulnerable and underserved communities. COVID-19 outcomes were found to be negatively associated with the number of people vaccinated and positively associated with age, the prevalence of cardiovascular disease, diabetes, and the minority population. There was also a strong positive correlation between unauthorized immigrants and the prevalence of COVID-19 cases and deaths. Meteorological variables were also investigated, but correlations with COVID-19 were relatively weak. Our findings suggest that COVID-19 has had a disproportionate impact across the US population among vulnerable and minority communities. Findings also emphasize the importance of vaccinations and tailored public health initiatives (e.g., mask mandates, vaccination) to reduce the spread of COVID-19 and the number of COVID-19 related deaths across all populations.

Project description:BackgroundIt is not clear which research misconduct policies are adopted by biomedical journals. This study assessed the prevalence and content policies of the most influential biomedical journals on misconduct and procedures for handling and responding to allegations of misconduct.MethodsWe conducted a cross-sectional study of misconduct policies of 399 high-impact biomedical journals in 27 biomedical categories of the Journal Citation Reports in December 2011. Journal websites were reviewed for information relevant to misconduct policies.ResultsOf 399 journals, 140 (35.1%) provided explicit definitions of misconduct. Falsification was explicitly mentioned by 113 (28.3%) journals, fabrication by 104 (26.1%), plagiarism by 224 (56.1%), duplication by 242 (60.7%) and image manipulation by 154 (38.6%). Procedures for responding to misconduct were described in 179 (44.9%) websites, including retraction, (30.8%) and expression of concern (16.3%). Plagiarism-checking services were used by 112 (28.1%) journals. The prevalences of all types of misconduct policies were higher in journals that endorsed any policy from editors' associations, Office of Research Integrity or professional societies compared to those that did not state adherence to these policy-producing bodies. Elsevier and Wiley-Blackwell had the most journals included (22.6% and 14.8%, respectively), with Wiley journals having greater a prevalence of misconduct definition and policies on falsification, fabrication and expression of concern and Elsevier of plagiarism-checking services.ConclusionsOnly a third of top-ranking peer-reviewed journals had publicly-available definitions of misconduct and less than a half described procedures for handling allegations of misconduct. As endorsement of international policies from policy-producing bodies was positively associated with implementation of policies and procedures, journals and their publishers should standardize their policies globally in order to increase public trust in the integrity of the published record in biomedicine.

Dataset Information

Quantifying the impact and extent of undocumented biomedical synonymy.

Publications

Quantifying the impact and extent of undocumented biomedical synonymy.

Similar Datasets

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