How user intelligence is improving PubMed

Fiorini, Nicolas; Leaman, Robert; Lipman, David J.; Lu, Zhiyong

doi:10.1038/nbt.4267

Cited by 65 publications

(42 citation statements)

References 45 publications

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“…Both are state-of-the-art language representation models and demonstrate promising results in NLP tasks of general purpose. We find that the BERT model pre-trained on PubMed abstracts (Fiorini et al, 2018) and MIMIC-III clinical notes achieves the best results, and is significantly superior to other models in the clinical domain. This demonstrates the importance of pre-training among different text genres.…”

Section: Introductionmentioning

confidence: 87%

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Peng

Yan

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We make the datasets, pre-trained models, and codes publicly available at https://github.com/ ncbi-nlp/BLUE_Benchmark.

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Section: Introductionmentioning

confidence: 87%

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Peng

Yan

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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587

462

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“…We compare the ranking of VIST with that of PubMed (using Entrez E-utilities [36], with returned documents sorted by their relevance to the query [37]) and that of a plain VSM ranking using Solr (KeywordScore). For queries containing more than one gene, we combined the resulting keywords with a logical OR in all systems.…”

Section: Resultsmentioning

confidence: 99%

VIST - a Variant-Information Search Tool for precision oncology

et al. 2019

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Background Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient’s genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of scientific publications focus on basic science and have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed for the targeted search of clinically relevant publications given an oncological mutation profile. Results VIST indexes all PubMed abstracts and content from ClinicalTrials.gov. It applies advanced text mining to identify mentions of genes, variants and drugs and uses machine learning based scoring to judge the clinical relevance of indexed abstracts. Its functionality is available through a fast and intuitive web interface. We perform several evaluations, showing that VIST’s ranking is superior to that of PubMed or a pure vector space model with regard to the clinical relevance of a document’s content. Conclusion Different user groups search repositories of scientific publications with different intentions. This diversity is not adequately reflected in the standard search engines, often leading to poor performance in specialized settings. We develop a search engine for the specific case of finding documents that are clinically relevant in the course of cancer treatment. We believe that the architecture of our engine, heavily relying on machine learning algorithms, can also act as a blueprint for search engines in other, equally specific domains. VIST is freely available at https://vist.informatik.hu-berlin.de/ Electronic supplementary material The online version of this article (10.1186/s12859-019-2958-3) contains supplementary material, which is available to authorized users.

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“…This AND tool has been integrated into PubMed, 11 but author identifiers are not displayed; they simply affect the ranking of retrieved results when query-by-author is done. After adding this feature, the click-through rate of citations in PubMed has increased [10,49]. One can observe that [5] and [10] are similar in the overall architecture and in many other respects.…”

Section: Detailed Reviewmentioning

confidence: 99%

A review of author name disambiguation techniques for the PubMed bibliographic database

Sanyal

Bhowmick

Das

2019

Journal of Information Science

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Author names in bibliographic databases often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. In this article, we focus on the research efforts targeted to disambiguate author names specifically in the PubMed bibliographic database. We believe this concentrated review will be useful to the research community because it discusses techniques applied to a very large real database that is actively used worldwide. We make a comprehensive survey of the existing author name disambiguation (AND) approaches that have been applied to the PubMed database: we organise the approaches into a taxonomy; describe the major characteristics of each approach including its performance, strengths, and limitations; and perform a comparative analysis of them. We also identify the datasets from PubMed that are publicly available for researchers to evaluate AND algorithms. Finally, we outline a few directions for future work.

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How user intelligence is improving PubMed

Cited by 65 publications

References 45 publications

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

VIST - a Variant-Information Search Tool for precision oncology

A review of author name disambiguation techniques for the PubMed bibliographic database

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