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Article ; Online: PubMed and beyond: biomedical literature search in the age of artificial intelligence.

Jin, Qiao / Leaman, Robert / Lu, Zhiyong

2024 Volume 100, Page(s) 104988

Abstract: Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded ... ...

Abstract	Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.
MeSH term(s)	Humans ; Artificial Intelligence ; Data Mining ; PubMed ; Delivery of Health Care ; Biomedical Research
Language	English
Publishing date	2024-02-01
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	2851331-9
ISSN	2352-3964
ISSN (online)	2352-3964
DOI	10.1016/j.ebiom.2024.104988
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Retrieve, Summarize, and Verify: How Will ChatGPT Affect Information Seeking from the Medical Literature?

Jin, Qiao / Leaman, Robert / Lu, Zhiyong

Journal of the American Society of Nephrology : JASN

2023 Volume 34, Issue 8, Page(s) 1302–1304

MeSH term(s)	Information Seeking Behavior ; Artificial Intelligence
Language	English
Publishing date	2023-05-31
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	1085942-1
ISSN	1533-3450 ; 1046-6673
ISSN (online)	1533-3450
ISSN	1046-6673
DOI	10.1681/ASN.0000000000000166
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3344: Show issues

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Book ; Online: PubMed and Beyond

Jin, Qiao / Leaman, Robert / Lu, Zhiyong

Recent Advances and Best Practices in Biomedical Literature Search

2023

Abstract: Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent ... ...

Abstract	Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent improvements in artificial intelligence have expanded functionality beyond keyword-based search, these advances may be unfamiliar to clinicians and researchers. In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs. We first examine the widely used PubMed search engine, discussing recent improvements and continued challenges. We then describe literature search tools catering to five specific information needs: 1. Identifying high-quality clinical research for evidence-based medicine. 2. Retrieving gene-related information for precision medicine and genomics. 3. Searching by meaning, including natural language questions. 4. Locating related articles with literature recommendation. 5. Mining literature to discover associations between concepts such as diseases and genetic variants. Additionally, we cover practical considerations and best practices for choosing and using these tools. Finally, we provide a perspective on the future of literature search engines, considering recent breakthroughs in large language models such as ChatGPT. In summary, our survey provides a comprehensive view of biomedical literature search functionalities with 36 publicly available tools. Comment: 27 pages, 6 figures, 36 tools
Keywords	Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Digital Libraries
Subject code	302
Publishing date	2023-07-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.

Luo, Ling / Wei, Chih-Hsuan / Lai, Po-Ting / Leaman, Robert / Chen, Qingyu / Lu, Zhiyong

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 5

Abstract: Motivation: Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction ... ...

Abstract	Motivation: Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g. gene or disease). Results: We therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to enhance the accuracy and stability of BioNER models. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g. the entire PubMed data). Availability and implementation: The source code, trained models and data for AIONER are freely available at https://github.com/ncbi/AIONER.
MeSH term(s)	Deep Learning ; Data Mining/methods ; Software ; Language ; PubMed
Language	English
Publishing date	2023-05-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Intramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad310
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 2374: Show issues

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Article ; Online: Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.

Leaman, Robert / Islamaj, Rezarta / Allot, Alexis / Chen, Qingyu / Wilbur, W John / Lu, Zhiyong

Patterns (New York, N.Y.)

2022 Volume 4, Issue 1, Page(s) 100659

Abstract: A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to ... ...

Abstract	A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid Collection shows that (1) most Long Covid articles do not refer to Long Covid by any name, (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid Collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid.
Language	English
Publishing date	2022-12-01
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2022.100659
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2

Leaman, Robert / Lu, Zhiyong

Abstract: The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high ... ...

Abstract	The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at https://github.com/ncbi-nlp/CovidTermVar.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Book ; Online: A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2

Leaman, Robert / Lu, Zhiyong

2020

Abstract	The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at https://github.com/ncbi-nlp/CovidTermVar. Comment: Accepted EMNLP NLP-COVID Workshop
Keywords	Computer Science - Digital Libraries ; Computer Science - Computation and Language
Subject code	006
Publishing date	2020-10-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.

Wei, Chih-Hsuan / Allot, Alexis / Lai, Po-Ting / Leaman, Robert / Tian, Shubo / Luo, Ling / Jin, Qiao / Wang, Zhizheng / Chen, Qingyu / Lu, Zhiyong

Nucleic acids research

2024

Abstract: PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It ... ...

Abstract	PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.
Language	English
Publishing date	2024-04-04
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkae235
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge.

Wei, Chih-Hsuan / Allot, Alexis / Lai, Po-Ting / Leaman, Robert / Tian, Shubo / Luo, Ling / Jin, Qiao / Wang, Zhizheng / Chen, Qingyu / Lu, Zhiyong

ArXiv

2024

Abstract	PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability.

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

PLoS biology

2020 Volume 18, Issue 6, Page(s) e3000716

Abstract: Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide ... ...

Abstract	Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips-and a web tool, PubReCheck-guiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors' work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck.
MeSH term(s)	Automation ; Data Mining ; Internet ; Software
Language	English
Publishing date	2020-06-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	2126776-5
ISSN	1545-7885 ; 1544-9173
ISSN (online)	1545-7885
ISSN	1544-9173
DOI	10.1371/journal.pbio.3000716
Database	MEDical Literature Analysis and Retrieval System OnLINE

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