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Journal articles on the topic 'Biomedical summarization'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Chaves, Andrea, Cyrille Kesiku, and Begonya Garcia-Zapirain. "Automatic Text Summarization of Biomedical Text Data: A Systematic Review." Information 13, no. 8 (August 19, 2022): 393. http://dx.doi.org/10.3390/info13080393.

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In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5% of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.

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Byrnes, Patrick D., and William Evan Higgins. "Efficient Bronchoscopic Video Summarization." IEEE Transactions on Biomedical Engineering 66, no. 3 (March 2019): 848–63. http://dx.doi.org/10.1109/tbme.2018.2859322.

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Guo, Yue, Wei Qiu, Yizhong Wang, and Trevor Cohen. "Automated Lay Language Summarization of Biomedical Scientific Reviews." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 160–68. http://dx.doi.org/10.1609/aaai.v35i1.16089.

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Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current effort, providing insights and directions for future work.

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Plaza, Laura, Mark Stevenson, and Alberto Díaz. "Resolving ambiguity in biomedical text to improve summarization." Information Processing & Management 48, no. 4 (July 2012): 755–66. http://dx.doi.org/10.1016/j.ipm.2011.09.005.

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Shang, Yue, Yanpeng Li, Hongfei Lin, and Zhihao Yang. "Enhancing Biomedical Text Summarization Using Semantic Relation Extraction." PLoS ONE 6, no. 8 (August 26, 2011): e23862. http://dx.doi.org/10.1371/journal.pone.0023862.

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Saini, Naveen, Sriparna Saha, Pushpak Bhattacharyya, and Himanshu Tuteja. "Textual Entailment--Based Figure Summarization for Biomedical Articles." ACM Transactions on Multimedia Computing, Communications, and Applications 16, no. 1s (April 28, 2020): 1–24. http://dx.doi.org/10.1145/3357334.

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Davoodijam, Ensieh, Nasser Ghadiri, Maryam Lotfi Shahreza, and Fabio Rinaldi. "MultiGBS: A multi-layer graph approach to biomedical summarization." Journal of Biomedical Informatics 116 (April 2021): 103706. http://dx.doi.org/10.1016/j.jbi.2021.103706.

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Du, Yongping, Qingxiao Li, Lulin Wang, and Yanqing He. "Biomedical-domain pre-trained language model for extractive summarization." Knowledge-Based Systems 199 (July 2020): 105964. http://dx.doi.org/10.1016/j.knosys.2020.105964.

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Wu, Xiaofang, Zhihao Yang, ZhiHeng Li, Hongfei Lin, and Jian Wang. "Disease Related Knowledge Summarization Based on Deep Graph Search." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/428195.

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The volume of published biomedical literature on disease related knowledge is expanding rapidly. Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher’s information needs. In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction forCarcinoma of bladderand outperforms the method of Combo.

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S. Almasoud, Ahmed, Siwar Ben Haj Hassine, Fahd N. Al-Wesabi, Mohamed K. Nour, Anwer Mustafa Hilal, Mesfer Al Duhayyim, Manar Ahmed Hamza, and Abdelwahed Motwakel. "Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model." Computers, Materials & Continua 71, no. 3 (2022): 5799–815. http://dx.doi.org/10.32604/cmc.2022.024556.

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Reeve, Lawrence H., Hyoil Han, and Ari D. Brooks. "The use of domain-specific concepts in biomedical text summarization." Information Processing & Management 43, no. 6 (November 2007): 1765–76. http://dx.doi.org/10.1016/j.ipm.2007.01.026.

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Afzal, Muhammad, Fakhare Alam, Khalid Mahmood Malik, and Ghaus M. Malik. "Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation." Journal of Medical Internet Research 22, no. 10 (October 23, 2020): e19810. http://dx.doi.org/10.2196/19810.

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Background Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. Objective Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. Methods In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. Results Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. Conclusions By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

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Plaza, Laura. "Comparing different knowledge sources for the automatic summarization of biomedical literature." Journal of Biomedical Informatics 52 (December 2014): 319–28. http://dx.doi.org/10.1016/j.jbi.2014.07.014.

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Moradi, Milad, and Nasser Ghadiri. "Different approaches for identifying important concepts in probabilistic biomedical text summarization." Artificial Intelligence in Medicine 84 (January 2018): 101–16. http://dx.doi.org/10.1016/j.artmed.2017.11.004.

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Moradi, Milad, and Nasser Ghadiri. "Quantifying the informativeness for biomedical literature summarization: An itemset mining method." Computer Methods and Programs in Biomedicine 146 (July 2017): 77–89. http://dx.doi.org/10.1016/j.cmpb.2017.05.011.

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Rouane, Oussama, Hacene Belhadef, and Mustapha Bouakkaz. "Combine clustering and frequent itemsets mining to enhance biomedical text summarization." Expert Systems with Applications 135 (November 2019): 362–73. http://dx.doi.org/10.1016/j.eswa.2019.06.002.

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Gupta, Supriya, Aakanksha Sharaff, and Naresh Kumar Nagwani. "Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity." Computer Systems Science and Engineering 45, no. 3 (2023): 2333–49. http://dx.doi.org/10.32604/csse.2023.030385.

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Mishra, Rashmi, Jiantao Bian, Marcelo Fiszman, Charlene R. Weir, Siddhartha Jonnalagadda, Javed Mostafa, and Guilherme Del Fiol. "Text summarization in the biomedical domain: A systematic review of recent research." Journal of Biomedical Informatics 52 (December 2014): 457–67. http://dx.doi.org/10.1016/j.jbi.2014.06.009.

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Nasr Azadani, Mozhgan, Nasser Ghadiri, and Ensieh Davoodijam. "Graph-based biomedical text summarization: An itemset mining and sentence clustering approach." Journal of Biomedical Informatics 84 (August 2018): 42–58. http://dx.doi.org/10.1016/j.jbi.2018.06.005.

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Moradi, Milad, Maedeh Dashti, and Matthias Samwald. "Summarization of biomedical articles using domain-specific word embeddings and graph ranking." Journal of Biomedical Informatics 107 (July 2020): 103452. http://dx.doi.org/10.1016/j.jbi.2020.103452.

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Ling, Xu, Jing Jiang, Xin He, Qiaozhu Mei, Chengxiang Zhai, and Bruce Schatz. "Generating gene summaries from biomedical literature: A study of semi-structured summarization." Information Processing & Management 43, no. 6 (November 2007): 1777–91. http://dx.doi.org/10.1016/j.ipm.2007.01.018.

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Moradi, Milad, Georg Dorffner, and Matthias Samwald. "Deep contextualized embeddings for quantifying the informative content in biomedical text summarization." Computer Methods and Programs in Biomedicine 184 (February 2020): 105117. http://dx.doi.org/10.1016/j.cmpb.2019.105117.

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Afzal, Muhammad, Maqbool Hussain, Khalid Mahmood Malik, and Sungyoung Lee. "Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study." JMIR Medical Informatics 7, no. 4 (December 9, 2019): e13430. http://dx.doi.org/10.2196/13430.

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Background The quality of health care is continuously improving and is expected to improve further because of the advancement of machine learning and knowledge-based techniques along with innovation and availability of wearable sensors. With these advancements, health care professionals are now becoming more interested and involved in seeking scientific research evidence from external sources for decision making relevant to medical diagnosis, treatments, and prognosis. Not much work has been done to develop methods for unobtrusive and seamless curation of data from the biomedical literature. Objective This study aimed to design a framework that can enable bringing quality publications intelligently to the users’ desk to assist medical practitioners in answering clinical questions and fulfilling their informational needs. Methods The proposed framework consists of methods for efficient biomedical literature curation, including the automatic construction of a well-built question, the recognition of evidence quality by proposing extended quality recognition model (E-QRM), and the ranking and summarization of the extracted evidence. Results Unlike previous works, the proposed framework systematically integrates the echelons of biomedical literature curation by including methods for searching queries, content quality assessments, and ranking and summarization. Using an ensemble approach, our high-impact classifier E-QRM obtained significantly improved accuracy than the existing quality recognition model (1723/1894, 90.97% vs 1462/1894, 77.21%). Conclusions Our proposed methods and evaluation demonstrate the validity and rigorousness of the results, which can be used in different applications, including evidence-based medicine, precision medicine, and medical education.

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Shen, Chen, Hongfei Lin, Huihui Hao, Zhihao Yang, Jian Wang, and Shaowu Zhang. "Intelligent multi-document summarization for biomedical literature by word embeddings and graph-based ranking." Journal of Intelligent & Fuzzy Systems 37, no. 4 (October 25, 2019): 4797–802. http://dx.doi.org/10.3233/jifs-179315.

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He, Huan, Sunyang Fu, Liwei Wang, Sijia Liu, Andrew Wen, and Hongfang Liu. "MedTator: a serverless annotation tool for corpus development." Bioinformatics 38, no. 6 (January 4, 2022): 1776–78. http://dx.doi.org/10.1093/bioinformatics/btab880.

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Abstract Summary Building a high-quality annotation corpus requires expenditure of considerable time and expertise, particularly for biomedical and clinical research applications. Most existing annotation tools provide many advanced features to cover a variety of needs where the installation, integration and difficulty of use present a significant burden for actual annotation tasks. Here, we present MedTator, a serverless annotation tool, aiming to provide an intuitive and interactive user interface that focuses on the core steps related to corpus annotation, such as document annotation, corpus summarization, annotation export and annotation adjudication. Availability and implementation MedTator and its tutorial are freely available from https://ohnlp.github.io/MedTator. MedTator source code is available under the Apache 2.0 license: https://github.com/OHNLP/MedTator. Supplementary information Supplementary data are available at Bioinformatics online.

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Li, Fang. "Design of an Interactive Two-Way Telemedicine Service System for Smart Home Care for the Elderly." Journal of Healthcare Engineering 2021 (April 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/6632865.

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In this paper, we deeply analyse and study the interactive telemedicine service system for elderly care in smart homes and design a data summarization method for large concurrent scenarios. The method first parses and reconstructs the data received by the system initially and then stores the reconstructed valid data into the local database, which realizes the fast data summarization under the heavy concurrency scenario. Secondly, a multiformat data adaptation method is designed for the problem that the data to be provided and processed are in various formats. The method uses a unified data format and adaptation process constraints to achieve centralized management of heterogeneous data from multiple sources, which provide a unified data support service for the system and upper-layer applications. Again, to deal with the application problem of highly correlated data, the data-sharing system provides data for each functional component of the telemedicine platform according to business requirements based on standardized data structure and unified storage management. This enables the barrier-free flow of multisource highly correlated data. When the consultation is in progress, the doctor can communicate with the patient with video and audio devices and, at the same time, can access the patient’s historical medical records and the medical records uploaded by the patient; after the consultation is completed, the consultation doctor needs to fill in the consultation record. The consultation assistance module can statistically analyse the workload of doctors and other information according to the background data, and the telemedicine system will play an increasingly important role in the medical and health care business.

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Workman, T. Elizabeth, Marcelo Fiszman, John F. Hurdle, and Thomas C. Rindflesch. "Biomedical text summarization to support genetic database curation: using Semantic MEDLINE to create a secondary database of genetic information." Journal of the Medical Library Association : JMLA 98, no. 4 (October 2010): 273–81. http://dx.doi.org/10.3163/1536-5050.98.4.003.

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Dumontier, Michel, Alasdair J. G. Gray, M. Scott Marshall, Vladimir Alexiev, Peter Ansell, Gary Bader, Joachim Baran, et al. "The health care and life sciences community profile for dataset descriptions." PeerJ 4 (August 16, 2016): e2331. http://dx.doi.org/10.7717/peerj.2331.

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Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

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Yang, Wei, Hua Yue, Guihong Lu, Wenjing Wang, Yuan Deng, Guanghui Ma, and Wei Wei. "Advances in Delivering Oxidative Modulators for Disease Therapy." Research 2022 (September 22, 2022): 1–24. http://dx.doi.org/10.34133/2022/9897464.

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Oxidation modulators regarding antioxidants and reactive oxygen species (ROS) inducers have been used for the treatment of many diseases. However, a systematic review that refers to delivery system for divergent modulation of oxidative level within the biomedical scope is lacking. To provide a comprehensive summarization and analysis, we review pilot designs for delivering the oxidative modulators and the main applications for inflammatory treatment and tumor therapy. On the one hand, the antioxidants based delivery system can be employed to downregulate ROS levels at inflammatory sites to treat inflammatory diseases (e.g., skin repair, bone-related diseases, organ dysfunction, and neurodegenerative diseases). On the other hand, the ROS inducers based delivery system can be employed to upregulate ROS levels at the tumor site to kill tumor cells (e.g., disrupt the endogenous oxidative balance and induce lethal levels of ROS). Besides the current designs of delivery systems for oxidative modulators and the main application cases, prospects for future research are also provided to identify intelligent strategies and inspire new concepts for delivering oxidative modulators.

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Vanegas, Jorge A., Sérgio Matos, Fabio González, and José L. Oliveira. "An Overview of Biomolecular Event Extraction from Scientific Documents." Computational and Mathematical Methods in Medicine 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/571381.

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This paper presents a review of state-of-the-art approaches to automatic extraction of biomolecular events from scientific texts. Events involving biomolecules such as genes, transcription factors, or enzymes, for example, have a central role in biological processes and functions and provide valuable information for describing physiological and pathogenesis mechanisms. Event extraction from biomedical literature has a broad range of applications, including support for information retrieval, knowledge summarization, and information extraction and discovery. However, automatic event extraction is a challenging task due to the ambiguity and diversity of natural language and higher-level linguistic phenomena, such as speculations and negations, which occur in biological texts and can lead to misunderstanding or incorrect interpretation. Many strategies have been proposed in the last decade, originating from different research areas such as natural language processing, machine learning, and statistics. This review summarizes the most representative approaches in biomolecular event extraction and presents an analysis of the current state of the art and of commonly used methods, features, and tools. Finally, current research trends and future perspectives are also discussed.

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Smalheiser, Neil R., Dean P. Fragnito, and Eric E. Tirk. "Anne O’Tate: Value-added PubMed search engine for analysis and text mining." PLOS ONE 16, no. 3 (March 8, 2021): e0248335. http://dx.doi.org/10.1371/journal.pone.0248335.

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Over a decade ago, we introduced Anne O’Tate, a free, public web-based tool http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi to support user-driven summarization, drill-down and mining of search results from PubMed, the leading search engine for biomedical literature. A set of hotlinked buttons allows the user to sort and rank retrieved articles according to important words in titles and abstracts; topics; author names; affiliations; journal names; publication year; and clustered by topic. Any result can be further mined by choosing any other button, and small search results can be expanded to include related articles. It has been deployed continuously, serving a wide range of biomedical users and needs, and over time has also served as a platform to support the creation of new tools that address additional needs. Here we describe the current, greatly expanded implementation of Anne O’Tate, which has added additional buttons to provide new functionalities: We now allow users to sort and rank search results by important phrases contained in titles and abstracts; the number of authors listed on the article; and pairs of topics that co-occur significantly more than chance. We also display articles according to NLM-indexed publication types, as well as according to 50 different publication types and study designs as predicted by a novel machine learning-based model. Furthermore, users can import search results into two new tools: e) Mine the Gap!, which identifies pairs of topics that are under-represented within set of the search results, and f) Citation Cloud, which for any given article, allows users to visualize the set of articles that cite it; that are cited by it; that are co-cited with it; and that are bibliographically coupled to it. We invite the scientific community to explore how Anne O’Tate can assist in analyzing biomedical literature, in a variety of use cases.

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Glicksberg, Benjamin S., Boris Oskotsky, Nicholas Giangreco, Phyllis M. Thangaraj, Vivek Rudrapatna, Debajyoti Datta, Remi Frazier, et al. "ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data." JAMIA Open 2, no. 1 (January 4, 2019): 10–14. http://dx.doi.org/10.1093/jamiaopen/ooy059.

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Abstract Objectives Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).

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Rodriguez-Pla, A. "AB0408 SYSTEMIC SCLERODERMA AND ENVIRONMENTAL RISK FACTORS: IDENTIFYING ASSOCIATIONS MINING THE BIOMEDICAL LITERATURE." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 1232.2–1233. http://dx.doi.org/10.1136/annrheumdis-2021-eular.330.

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Background:A debate still exists concerning the role of occupational and environmental factors in the pathogenesis of systemic scleroderma (SSc).Objectives:Our aim was to explore associations between SSc and environmental factors utilizing an automatic semantic interpretation of PubMed results.Methods:The literature search string: (“systemic sclerosis” OR “scleroderma”) AND (“occupational exposure” OR “environmental” OR “risk factor”) was used to retrieve abstracts from the entire PubMed database, using Semantic MEDLINE 2, on 6/14/2020. This application represents a network of semantic predications (triples of the form subject-predicate (or relation) -object, e.g. Occupational Exposure causes Systemic Scleroderma) on a knowledge graph. Subject and object arguments of each predication are concepts from the Unified Medical Language System (UMLS) Metathesaurus and the relation is taken from the UMLS Semantic Network. The system allows for choosing the central topic (“Systemic Scleroderma”), the length of the network (3 nodes), and automatic summarization, eliminating the less informative predications.Results:The search string retrieved 864 citations and identified 6,397 predications by using 34 types of relations. Initially, we focused our attention on the ‘CAUSES’ type of relation (Figure 1), displaying a network with 59 nodes and 57 edges.The central concepts of this network, identified as having causal relationship with SSc are autoimmune diseases/autoimmunity, chemicals such as bleomycin, occupational and environmental exposure, especially silica, vinyl chloride and trichloroethylene, genes, including HLA and non-HLA genes, genetic polymorphisms, transcription factors (TFs) such as Fli1 and KLF5, and fibrosis. Eosinophilia-myalgia syndrome, toxic oil syndrome and infection were all causally linked to autoimmune diseases. Minerals were associated with occupational exposure and with autoimmune diseases. Concepts causally linked to fibrosis were rare diseases, HLA genes, other non-HLA genes, such as STAT4, IR4, IR5, TLR4, TLR7 and Rho-associated Kinase, and vinyl chloride monomer. Pathogenic factors associated with SSc were endothelial dysfunction and extracellular matrix proteins. Many of the papers in the network also suggested that hormonal factors are involved.Conclusion:Inspection on the knowledge graphs reveals concepts central to research on the etiopathogenesis of SSc. The relations in which these concepts participate, provide more specific information. The Semantic MEDLINE graph supports the kind of patterns that underpin literature-based discovery.Although the pathogenesis of SSc remains elusive, it is accepted that initial vascular damage driven by autoimmunity and environmental factors causes abnormalities in the vasculature resulting in the activation of fibroblasts in various organs. Silica and solvents such as trichloroethylene seem to be the most consistently suspected environmental agents in SSc.References:[1]Rindflesch TC,et al. Semantic MEDLINE: An advanced information management application for biomedicine. Information Services & Use 2011;31:15-21.Figure 1.Semantic Network of Casual Relationships of Systemic Scleroderma.Disclosure of Interests:None declared

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Rodriguez-Pla, A., and R. Cartin-Ceba. "AB0386 CORONAVIRUS INFECTION AND VASCULITIS: IDENTIFYING ASSOCIATIONS MINING THE BIOMEDICAL LITERATURE." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 1220.1–1221. http://dx.doi.org/10.1136/annrheumdis-2021-eular.329.

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Background:Based on recent publications suggesting an association between COVID-19 and vascularInflammation.Objectives:Our aim was to explore new associations between coronavirus infections and vasculitis utilizing semantic mining of PubMed results.Methods:The following literature search string: “(vasculitis OR vascular inflammation OR vascular damage) AND (coronavirus OR SARS virus OR MERS-CoV OR Covid-19)” was used to retrieve abstracts from the whole PubMed database, using Semantic MEDLINE 2. on 6/7/2020. This application represents a network of semantic predications (triples of the form subject-predicate-object, e.g. COVID-19 causes Disease) on a knowledge graph. The system allows for choosing the maximum number of nodes represented, the central topic, and the length of the network. For our network we chose to display all relations, COVID-19 (31 edges) as the central term, 3 lengths, and selecting the most informative nodes. Automatic summarization eliminated the less informative predications.Results:The search string retrieved 152 citations from PubMed and identified 1,028 predications. Thenetwork (Figure 1), displayed using COVID-19 as the central term, consisted of 72 nodes and 140 edges. The 5 most connected nodes were ’Patients: 19 nodes’, COVID-19: 13’, ‘Inflammation: 13’, ’Lung: 11’, and ‘Disease: 11’. Multiple links have been found between coronavirus and vasculitis. Animal coronaviruses, including the one causing feline infectious peritonitis (FIP), the murine coronavirus mouse hepatitis virus (MHV), the SARS-CoV in transgenic mice and coronavirus in ferrets, are known to cause vasculitis in animals. It is known that coronaviruses that infect animals can evolve and become new human coronaviruses. SARS produces inflammation in blood vessels. In 2005, a link between the coronavirus HCoV-NL63 or New Haven Coronavirus (HCoV-NH) and KD was reported,although later studies concluded that HCoV-NH did not play a dominant role in the etiology orpathogenesis of KD. In 2014, serological testing suggested the possible involvement of CoV-229E in the development of KD. There has also been a report of KD patients being infected by coronavirus OC43/HKU1.COVID-19 may infect the vessels and trigger inflammatory reactions like those of vasculitis, including vasculitis-like cutaneous lesions. COVID-19 patients develop thrombosis, and increased risk of thrombosis is also present in primary vasculitic syndromes. Children, many of whom tested positive for COVID-19 antibodies, developed Multisystem Inflammatory Syndrome in Children (MIS-C), an inflammatory condition similar to Kawasaki Disease (KD).Conclusion:Knowledge integration and discovery methods are an efficient and powerful way of retrieving and analyzing relevan information from multiple papers. Their main advantages are finding relations among biomedical concepts, generating new hypotheses, and opening them to literature-based discovery.SARS-CoV-2 may cause vasculitis or vasculitis-like syndromes. The KD-like syndrome reported mainly in children with COVID-19 revives the previous suspicion of coronavirus as a possible triggering agent of KD and the decades-old hypothesis of infection involvement in the pathogenesis of vasculitis.References:[1]Rindflesch TC,et al. Semantic MEDLINE: An advanced information management application for biomedicine. Information Services & Use 2011;31:15-21.Figure 1.Semantic Networks Resulting from Pubmed. All relations COVID-19 (edges:140) 3 lengths Max nodes: F (all nodes considered relevant).Disclosure of Interests:None declared

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Papadopoulos, Dimitris, Nikolaos Papadakis, and Antonis Litke. "A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case." Applied Sciences 10, no. 16 (August 13, 2020): 5630. http://dx.doi.org/10.3390/app10165630.

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The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.

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Yang, Xi, Xing He, Hansi Zhang, Yinghan Ma, Jiang Bian, and Yonghui Wu. "Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models." JMIR Medical Informatics 8, no. 11 (November 23, 2020): e19735. http://dx.doi.org/10.2196/19735.

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Background Semantic textual similarity (STS) is one of the fundamental tasks in natural language processing (NLP). Many shared tasks and corpora for STS have been organized and curated in the general English domain; however, such resources are limited in the biomedical domain. In 2019, the National NLP Clinical Challenges (n2c2) challenge developed a comprehensive clinical STS dataset and organized a community effort to solicit state-of-the-art solutions for clinical STS. Objective This study presents our transformer-based clinical STS models developed during this challenge as well as new models we explored after the challenge. This project is part of the 2019 n2c2/Open Health NLP shared task on clinical STS. Methods In this study, we explored 3 transformer-based models for clinical STS: Bidirectional Encoder Representations from Transformers (BERT), XLNet, and Robustly optimized BERT approach (RoBERTa). We examined transformer models pretrained using both general English text and clinical text. We also explored using a general English STS dataset as a supplementary corpus in addition to the clinical training set developed in this challenge. Furthermore, we investigated various ensemble methods to combine different transformer models. Results Our best submission based on the XLNet model achieved the third-best performance (Pearson correlation of 0.8864) in this challenge. After the challenge, we further explored other transformer models and improved the performance to 0.9065 using a RoBERTa model, which outperformed the best-performing system developed in this challenge (Pearson correlation of 0.9010). Conclusions This study demonstrated the efficiency of utilizing transformer-based models to measure semantic similarity for clinical text. Our models can be applied to clinical applications such as clinical text deduplication and summarization.

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Long, Shengxiang, Yongmin Peng, Jing Lu, Tong Zhu, Chuanxiang Sun, and Jun Luo. "Identification and Applications of Micro to Macroscale Shale Lithofacies." Journal of Nanoscience and Nanotechnology 21, no. 1 (January 1, 2021): 659–69. http://dx.doi.org/10.1166/jnn.2021.18477.

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Systematic research and evaluations of shale gas reservoirs are critical in shale gas exploration and development. Previous studies in this field mainly depend on experimental analysis that is often overly simplified. Here, we report an integrated geological and engineering method that combines the lithofacies division system with an identification and evaluation technology from the micro to macroscale. This method has been successfully applied in field sites. The key achievements of this method include the following: 1 Lithofacies refers to the rock or rock assemblage formed in a specific depositional environment that has experienced a certain degree of diagenesis, which is a comprehensive term that contains information about the lithology, physical properties, gas content and fracturability. With the main rock type or several rock types combined as the basic name, the shale lithofacies are classified and named by highlighting particular characteristics such as total organic carbon (TOC) and content of brittle minerals. 2 Based on the lithofacies classification and constrained by the equivalent sequence interfaces or thin layer interfaces, the organic matter-rich shales in the Upper Ordovician Wufeng Formation to the Lower Silurian Longmaxi Formation in the Fuling gas field are longitudinally divided into 7 shale lithofacies. Furthermore, the organic carbonrich, high-silicon lithofacies is identified as the most favorable type for shale gas reservoirs. 3 A set of marine shale lithofacies identification technologies was created using cross-referenced scales of conventional logging and image logging, as well as logging summarization. Lastly, 4 the lithofacies technologies reported here have been successfully applied in multiple areas, including in the south and southeast of the Sichuan Basin and the Pengshui area. Applications in these areas include shale formation comparison and analysis, monitoring and analysis of horizontal well drilling, and assessment of fracturing stages in horizontal sections. The lithofacies technology is proven to be efficient and applicable for comprehensive shale gas reservoir evaluation.

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Wang, Yanshan, Sunyang Fu, Feichen Shen, Sam Henry, Ozlem Uzuner, and Hongfang Liu. "The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview." JMIR Medical Informatics 8, no. 11 (November 27, 2020): e23375. http://dx.doi.org/10.2196/23375.

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Background Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval. Objective Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain. Methods We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium. Results Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs. Conclusions The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.

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Givchi, Azadeh, Reza Ramezani, and Ahmad Baraani. "Graph-based Abstractive Biomedical Text Summarization." Journal of Biomedical Informatics, June 2022, 104099. http://dx.doi.org/10.1016/j.jbi.2022.104099.

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Wang, Mengqian, Manhua Wang, Fei Yu, Yue Yang, Jennifer Walker, and Javed Mostafa. "A systematic review of automatic text summarization for biomedical literature and EHRs." Journal of the American Medical Informatics Association, August 2, 2021. http://dx.doi.org/10.1093/jamia/ocab143.

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Abstract Objective Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents’ essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research. Materials and Methods This review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation. Results Fifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics. Discussion and Conclusion This study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature.

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Lee, Eva K., and Karan Uppal. "CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text." BMC Medical Informatics and Decision Making 20, S14 (December 2020). http://dx.doi.org/10.1186/s12911-020-01330-8.

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Abstract Background Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. Methods A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. Results The random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. Conclusions We have developed a web-based summarization and visualization tool, CERC (https://newton.isye.gatech.edu/CERC1/), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

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Yoo, Illhoi, Xiaohua Hu, and Il-Yeol Song. "A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method." BMC Bioinformatics 8, S9 (November 27, 2007). http://dx.doi.org/10.1186/1471-2105-8-s9-s4.

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Xie, Qianqian, Jennifer Amy Bishop, Prayag Tiwari, and Sophia Ananiadou. "Pre-trained language models with domain knowledge for biomedical extractive summarization." Knowledge-Based Systems, July 2022, 109460. http://dx.doi.org/10.1016/j.knosys.2022.109460.

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Gupta, Supriya, Aakanksha Sharaff, and Naresh Kumar Nagwani. "Frequent item-set mining and clustering based ranked biomedical text summarization." Journal of Supercomputing, July 4, 2022. http://dx.doi.org/10.1007/s11227-022-04578-1.

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Gupta, Supriya. "Frequent Item-Set Mining and Clustering Based Ranked Biomedical Text Summarization." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4067265.

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Zhang, Han, Marcelo Fiszman, Dongwook Shin, Bartlomiej Wilkowski, and Thomas C. Rindflesch. "Clustering cliques for graph-based summarization of the biomedical research literature." BMC Bioinformatics 14, no. 1 (June 7, 2013). http://dx.doi.org/10.1186/1471-2105-14-182.

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Zheng, Ling, Hua Min, Yan Chen, Vipina Keloth, James Geller, Yehoshua Perl, and George Hripcsak. "Outlier concepts auditing methodology for a large family of biomedical ontologies." BMC Medical Informatics and Decision Making 20, S10 (December 2020). http://dx.doi.org/10.1186/s12911-020-01311-x.

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Abstract Background Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). Methods To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. Results We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT’s Specimen hierarchy and NCIt’s Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the “six out of six” condition required to show the scalability for the whole family. Conclusions We have shown that the small partial-area technique can be potentially successful for the family of ontologies with outgoing lateral relationships in BioPortal, thus improve the scalability of this QA technique.

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Yu, Yue, Kexin Huang, Chao Zhang, Lucas M. Glass, Jimeng Sun, and Cao Xiao. "SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization." Bioinformatics, March 26, 2021. http://dx.doi.org/10.1093/bioinformatics/btab207.

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Abstract Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. Results To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. Availability and implementation The code is available in Supplementary Material. Supplementary information Supplementary data are available at Bioinformatics online.

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Rohil, Mukesh Kumar, and Varun Magotra. "An exploratory study of automatic text summarization in biomedical and healthcare domain." Healthcare Analytics, April 2022, 100058. http://dx.doi.org/10.1016/j.health.2022.100058.

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Du, Yongping, Yiliang Zhao, Jingya Yan, and Qingxiao Li. "UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain." Methods, April 2022. http://dx.doi.org/10.1016/j.ymeth.2022.03.012.

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