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Annotation of epilepsy clinic letters for natural language processing J. Biomed. Semant. (IF 1.6) Pub Date : 2024-09-15 Beata Fonferko-Shadrach, Huw Strafford, Carys Jones, Russell A. Khan, Sharon Brown, Jenny Edwards, Jonathan Hawken, Luke E. Shrimpton, Catharine P. White, Robert Powell, Inder M. S. Sawhney, William O. Pickrell, Arron S. Lacey
Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the development and validation of NLP applications is limited. We created synthetic clinical documents to address this, and to validate the Extraction of Epilepsy Clinical
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An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology J. Biomed. Semant. (IF 1.6) Pub Date : 2024-08-30 Guglielmo Faggioli, Laura Menotti, Stefano Marchesin, Adriano Chió, Arianna Dagliati, Mamede de Carvalho, Marta Gromicho, Umberto Manera, Eleonora Tavazzi, Giorgio Maria Di Nunzio, Gianmaria Silvello, Nicola Ferro
Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable
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Concretizing plan specifications as realizables within the OBO foundry J. Biomed. Semant. (IF 1.6) Pub Date : 2024-08-20 William D. Duncan, Matthew Diller, Damion Dooley, William R. Hogan, John Beverley
Within the Open Biological and Biomedical Ontology (OBO) Foundry, many ontologies represent the execution of a plan specification as a process in which a realizable entity that concretizes the plan specification, a “realizable concretization” (RC), is realized. This representation, which we call the “RC-account”, provides a straightforward way to relate a plan specification to the entity that bears
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Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models J. Biomed. Semant. (IF 1.6) Pub Date : 2024-08-10 Jianfu Li, Yiming Li, Yuanyi Pan, Jinjing Guo, Zenan Sun, Fang Li, Yongqun He, Cui Tao
Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. ClinicalTrials.gov is a valuable repository of clinical trial information, but the vaccine data
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Chemical entity normalization for successful translational development of Alzheimer’s disease and dementia therapeutics J. Biomed. Semant. (IF 1.6) Pub Date : 2024-07-31 Sarah Mullin, Robert McDougal, Kei-Hoi Cheung, Halil Kilicoglu, Amanda Beck, Caroline J. Zeiss
Identifying chemical mentions within the Alzheimer’s and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that
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Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis J. Biomed. Semant. (IF 1.6) Pub Date : 2024-06-19 Jie Zheng, Xingxian Li, Anna Maria Masci, Hayleigh Kahn, Anthony Huffman, Eliyas Asfaw, Yuanyi Pan, Jinjing Guo, Virginia He, Justin Song, Andrey I. Seleznev, Asiyah Yu Lin, Yongqun He
The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity of cancer vaccine data significantly impedes effective integration and analysis. While CanVaxKB serves as a pioneering database for over 670 manually annotated cancer vaccines, it is important to distinguish that a database, on its own, does not offer the structured
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Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection J. Biomed. Semant. (IF 1.6) Pub Date : 2024-06-07 Abdullateef I. Almudaifer, Whitney Covington, JaMor Hairston, Zachary Deitch, Ankit Anand, Caleb M. Carroll, Estera Crisan, William Bradford, Lauren A. Walter, Ellen F. Eaton, Sue S. Feldman, John D. Osborne
The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject. Existing models for determining modifiers of clinical entities involve regular expression or features weights that are trained independently for each modifier. We develop and evaluate a multi-task transformer architecture design
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Correction to: Semantic units: organizing knowledge graphs into semantically meaningful units of representation J. Biomed. Semant. (IF 1.6) Pub Date : 2024-06-06 Lars Vogt, Tobias Kuhn, Robert Hoehndorf
Correction to: Journal of Biomedical Semantics (2024) 15:7 https://doi.org/10.1186/s13326-024-00310-5 Following publication of the original article [1], we have been notified that the authors’ first and last names were switched and published incorrectly. It is now: Vogt Lars1*, Kuhn Tobias2 and Hoehndorf Robert3 It should be: Lars Vogt1*, Tobias Kuhn2 and Robert Hoehndorf3 Vogt et al. (2024) Semantic
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Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflows J. Biomed. Semant. (IF 1.6) Pub Date : 2024-06-06 Mathias De Brouwer, Pieter Bonte, Dörthe Arndt, Miel Vander Sande, Anastasia Dimou, Ruben Verborgh, Filip De Turck, Femke Ongenae
In healthcare, an increasing collaboration can be noticed between different caregivers, especially considering the shift to homecare. To provide optimal patient care, efficient coordination of data and workflows between these different stakeholders is required. To achieve this, data should be exposed in a machine-interpretable, reusable manner. In addition, there is a need for smart, dynamic, personalized
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Explanatory argumentation in natural language for correct and incorrect medical diagnoses J. Biomed. Semant. (IF 1.6) Pub Date : 2024-05-30 Benjamin Molinet, Santiago Marro, Elena Cabrio, Serena Villata
A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to
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Semantic units: organizing knowledge graphs into semantically meaningful units of representation J. Biomed. Semant. (IF 1.6) Pub Date : 2024-05-27 Vogt Lars, Kuhn Tobias, Hoehndorf Robert
In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. We introduce “semantic units” as
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Leveraging logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies J. Biomed. Semant. (IF 1.6) Pub Date : 2024-05-01 Rashmie Abeysinghe, Fengbo Zheng, Jay Shi, Samden D. Lhatoo, Licong Cui
Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A relations in a biomedical terminology could be detrimental to its downstream usages. In this paper, we investigate an approach combining logical definitions and lexical features to discover missing IS-A relations in two biomedical terminologies: SNOMED CT and the National Cancer Institute (NCI) thesaurus. The method
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Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery J. Biomed. Semant. (IF 1.6) Pub Date : 2024-05-01 Daniel N. Sosa, Georgiana Neculae, Julien Fauqueur, Russ B. Altman
Leveraging AI for synthesizing the deluge of biomedical knowledge has great potential for pharmacological discovery with applications including developing new therapeutics for untreated diseases and repurposing drugs as emergent pandemic treatments. Creating knowledge graph representations of interacting drugs, diseases, genes, and proteins enables discovery via embedding-based ML approaches and link
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Ontological representation, modeling, and analysis of parasite vaccines J. Biomed. Semant. (IF 1.6) Pub Date : 2024-04-25 Anthony Huffman, Xumeng Zhang, Meghana Lanka, Jie Zheng, Anna Maria Masci, Yongqun He
Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase
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Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials J. Biomed. Semant. (IF 1.6) Pub Date : 2024-04-23 Christian Witte, David M. Schmidt, Philipp Cimiano
Systematic reviews of Randomized Controlled Trials (RCTs) are an important part of the evidence-based medicine paradigm. However, the creation of such systematic reviews by clinical experts is costly as well as time-consuming, and results can get quickly outdated after publication. Most RCTs are structured based on the Patient, Intervention, Comparison, Outcomes (PICO) framework and there exist many
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RecSOI: recommending research directions using statements of ignorance J. Biomed. Semant. (IF 1.6) Pub Date : 2024-04-22 Adrien Bibal, Nourah M. Salem, Rémi Cardon, Elizabeth K. White, Daniel E. Acuna, Robin Burke, Lawrence E. Hunter
The more science advances, the more questions are asked. This compounding growth can make it difficult to keep up with current research directions. Furthermore, this difficulty is exacerbated for junior researchers who enter fields with already large bases of potentially fruitful research avenues. In this paper, we propose a novel task and a recommender system for research directions, RecSOI, that
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Enriching the FIDEO ontology with food-drug interactions from online knowledge sources J. Biomed. Semant. (IF 1.6) Pub Date : 2024-03-04 Rabia Azzi, Georgeta Bordea, Romain Griffier, Jean Noël Nikiema, Fleur Mougin
The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and
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The use of foundational ontologies in biomedical research J. Biomed. Semant. (IF 1.6) Pub Date : 2023-12-11 César H. Bernabé, Núria Queralt-Rosinach, Vítor E. Silva Souza, Luiz Olavo Bonino da Silva Santos, Barend Mons, Annika Jacobsen, Marco Roos
The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level)
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BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs J. Biomed. Semant. (IF 1.6) Pub Date : 2023-12-08 Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg, Michael Cochez, Paul Groth
Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such
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Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases J. Biomed. Semant. (IF 1.6) Pub Date : 2023-12-05 Shuxin Zhang, Nirupama Benis, Ronald Cornet
Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and
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Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph J. Biomed. Semant. (IF 1.6) Pub Date : 2023-11-28 Gollam Rabby, Jennifer D’Souza, Allard Oelen, Lucie Dvorackova, Vojtěch Svátek, Sören Auer
Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents
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Data management plans as linked open data: exploiting ARGOS FAIR and machine actionable outputs in the OpenAIRE research graph J. Biomed. Semant. (IF 1.6) Pub Date : 2023-11-02 Elli Papadopoulou, Alessia Bardi, George Kakaletris, Diamadis Tziotzios, Paolo Manghi, Natalia Manola
Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes, institutions) and the relationships among them. They present a contextualized view of current research that supports discovery, re-use, reproducibility, monitoring, transparency and omni-comprehensive assessment. A Data Management Plan (DMP)
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Context-based refinement of mappings in evolving life science ontologies J. Biomed. Semant. (IF 1.6) Pub Date : 2023-10-19 Victor Eiti Yamamoto, Juliana Medeiros Destro, Julio Cesar dos Reis
Biomedical computational systems benefit from ontologies and their associated mappings. Indeed, aligned ontologies in life sciences play a central role in several semantic-enabled tasks, especially in data exchange. It is crucial to maintain up-to-date alignments according to new knowledge inserted in novel ontology releases. Refining ontology mappings in place, based on adding concepts, demands further
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Analysis and implementation of the DynDiff tool when comparing versions of ontology J. Biomed. Semant. (IF 1.6) Pub Date : 2023-09-28 Sara Diaz Benavides, Silvio D. Cardoso, Marcos Da Silveira, Cédric Pruski
Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to understand and manage these changes and their impact on depending systems well in order to handle the growing volume of data annotated
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Development and validation of the early warning system scores ontology J. Biomed. Semant. (IF 1.6) Pub Date : 2023-09-20 Cilia E. Zayas, Justin M. Whorton, Kevin W. Sexton, Charles D. Mabry, S. Clint Dowland, Mathias Brochhausen
Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations
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Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments J. Biomed. Semant. (IF 1.6) Pub Date : 2023-09-01 Mariana Neves, Antonina Klippert, Fanny Knöspel, Juliane Rudeck, Ailine Stolz, Zsofia Ban, Markus Becker, Kai Diederich, Barbara Grune, Pia Kahnau, Nils Ohnesorge, Johannes Pucher, Gilbert Schönfelder, Bettina Bert, Daniel Butzke
Current animal protection laws require replacement of animal experiments with alternative methods, whenever such methods are suitable to reach the intended scientific objective. However, searching for alternative methods in the scientific literature is a time-consuming task that requires careful screening of an enormously large number of experimental biomedical publications. The identification of potentially
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Automatic transparency evaluation for open knowledge extraction systems J. Biomed. Semant. (IF 1.6) Pub Date : 2023-08-31 Maryam Basereh, Annalina Caputo, Rob Brennan
This paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built
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Multi-domain knowledge graph embeddings for gene-disease association prediction J. Biomed. Semant. (IF 1.6) Pub Date : 2023-08-14 Susana Nunes, Rita T. Sousa, Catia Pesquita
Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of genes and diseases based on the scientific knowledge described in ontologies, which can then be explored by machine learning algorithms. However, state-of-the-art
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An extension of the BioAssay Ontology to include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows J. Biomed. Semant. (IF 1.6) Pub Date : 2023-08-11 Steve Penn, Jane Lomax, Anneli Karlsson, Vincent Antonucci, Carl-Dieter Zachmann, Samantha Kanza, Stephan Schurer, John Turner
With the capacity to produce and record data electronically, Scientific research and the data associated with it have grown at an unprecedented rate. However, despite a decent amount of data now existing in an electronic form, it is still common for scientific research to be recorded in an unstructured text format with inconsistent context (vocabularies) which vastly reduces the potential for direct
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Improving the classification of cardinality phenotypes using collections J. Biomed. Semant. (IF 1.6) Pub Date : 2023-08-07 Sarah M. Alghamdi, Robert Hoehndorf
Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide
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Semantically enabling clinical decision support recommendations J. Biomed. Semant. (IF 1.6) Pub Date : 2023-07-18 Oshani Seneviratne, Amar K. Das, Shruthi Chari, Nkechinyere N. Agu, Sabbir M. Rashid, Jamie McCusker, Jade S. Franklin, Miao Qi, Kristin P. Bennett, Ching-Hua Chen, James A. Hendler, Deborah L. McGuinness
Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding
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FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards J. Biomed. Semant. (IF 1.6) Pub Date : 2023-07-01 Alban Gaignard, Thomas Rosnet, Frédéric De Lamotte, Vincent Lefort, Marie-Dominique Devignes
The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition
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Features of a FAIR vocabulary J. Biomed. Semant. (IF 1.6) Pub Date : 2023-06-01 Fuqi Xu, Nick Juty, Carole Goble, Simon Jupp, Helen Parkinson, Mélanie Courtot
The Findable, Accessible, Interoperable and Reusable(FAIR) Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able to define FAIR vocabularies, identify features of FAIR vocabularies, and provide assessment approaches against the features can guide the development of vocabularies. We differentiate data, data resources
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Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature J. Biomed. Semant. (IF 1.6) Pub Date : 2023-05-30 Weixin Xie, Kunjie Fan, Shijun Zhang, Lang Li
Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are
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Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets J. Biomed. Semant. (IF 1.6) Pub Date : 2023-04-18 Enayat Rajabi, Rishi Midha, Jairo Francisco de Souza
The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the
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The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond J. Biomed. Semant. (IF 1.6) Pub Date : 2023-02-24 Lauren E. Chan, Anne E. Thessen, William D. Duncan, Nicolas Matentzoglu, Charles Schmitt, Cynthia J. Grondin, Nicole Vasilevsky, Julie A. McMurry, Peter N. Robinson, Christopher J. Mungall, Melissa A. Haendel
Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data.
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MedLexSp – a medical lexicon for Spanish medical natural language processing J. Biomed. Semant. (IF 1.6) Pub Date : 2023-02-02 Leonardo Campillos-Llanos
Medical lexicons enable the natural language processing (NLP) of health texts. Lexicons gather terms and concepts from thesauri and ontologies, and linguistic data for part-of-speech (PoS) tagging, lemmatization or natural language generation. To date, there is no such type of resource for Spanish. This article describes an unified medical lexicon for Medical Natural Language Processing in Spanish
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Classifying literature mentions of biological pathogens as experimentally studied using natural language processing J. Biomed. Semant. (IF 1.6) Pub Date : 2023-01-31 Antonio Jose Jimeno Yepes, Karin Verspoor
Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, identifying the literature that specifically characterises these pathogens and their properties based on experimental research, important for understanding of the molecular basis of diseases caused by
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We are not ready yet: limitations of state-of-the-art disease named entity recognizers J. Biomed. Semant. (IF 1.6) Pub Date : 2022-10-27 Kühnel, Lisa, Fluck, Juliane
Intense research has been done in the area of biomedical natural language processing. Since the breakthrough of transfer learning-based methods, BERT models are used in a variety of biomedical and clinical applications. For the available data sets, these models show excellent results - partly exceeding the inter-annotator agreements. However, biomedical named entity recognition applied on COVID-19
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A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology J. Biomed. Semant. (IF 1.6) Pub Date : 2022-10-21 He, Yongqun, Yu, Hong, Huffman, Anthony, Lin, Asiyah Yu, Natale, Darren A., Beverley, John, Zheng, Ling, Perl, Yehoshua, Wang, Zhigang, Liu, Yingtong, Ong, Edison, Wang, Yang, Huang, Philip, Tran, Long, Du, Jinyang, Shah, Zalan, Shah, Easheta, Desai, Roshan, Huang, Hsin-hui, Tian, Yujia, Merrell, Eric, Duncan, William D., Arabandi, Sivaram, Schriml, Lynn M., Zheng, Jie, Masci, Anna Maria, Wang, Liwei
The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus
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Alignment of vaccine codes using an ontology of vaccine descriptions J. Biomed. Semant. (IF 1.6) Pub Date : 2022-10-18 Becker, Benedikt FH, Kors, Jan A, van Mulligen, Erik M, Sturkenboom, Miriam CJM
Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines. We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology
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Pathling: analytics on FHIR J. Biomed. Semant. (IF 1.6) Pub Date : 2022-09-08 Grimes, John, Szul, Piotr, Metke-Jimenez, Alejandro, Lawley, Michael, Loi, Kylynn
Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in the task of analyzing Fast Healthcare Interoperability Resources (FHIR®) data and associated clinical terminology
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Identification of missing hierarchical relations in the vaccine ontology using acquired term pairs J. Biomed. Semant. (IF 1.6) Pub Date : 2022-08-13 Manuel, Warren, Abeysinghe, Rashmie, He, Yongqun, Tao, Cui, Cui, Licong
The Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applications that it is being used in. Quality assurance of VO is imperative to ensure that it provides accurate domain knowledge to these downstream tasks. Manual review to identify and fix quality issues (such as missing hierarchical is-a relations) is challenging given
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DCSO: towards an ontology for machine-actionable data management plans J. Biomed. Semant. (IF 1.6) Pub Date : 2022-07-26 Cardoso, João, Castro, Leyla J., Ekaputra, Fajar J., Jacquemot, Marie C., Suchánek, Marek, Miksa, Tomasz, Borbinha, José
The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic
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Correction: PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature J. Biomed. Semant. (IF 1.6) Pub Date : 2022-07-20 Binkheder, Samar, Wu, Heng-Yi, Quinney, Sara K., Zhang, Shijun, Zitu, Md. Muntasir, Chiang, Chien-Wei, Wang, Lei, Jones, Josette, Li, Lang
Correction: J Biomed Semantics 13, 17 (2022) https://doi.org/10.1186/s13326-022-00272-6 Following publication of the original article [1], we have been notified that the numbering of column 1 in Table 2 is incorrect. It should be as follows: 2. Intermediate category (n =2) 2.1. Data sources 2.2. Study design or IRB 3. Exclusion category (n =3) 3.1. Exclusion 1– Irrelative evidence: 3.1.1. Location
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Performance assessment of ontology matching systems for FAIR data J. Biomed. Semant. (IF 1.6) Pub Date : 2022-07-15 van Damme, Philip, Fernández-Breis, Jesualdo Tomás, Benis, Nirupama, Miñarro-Gimenez, Jose Antonio, de Keizer, Nicolette F., Cornet, Ronald
Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application
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Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research J. Biomed. Semant. (IF 1.6) Pub Date : 2022-06-27 Vogt, Lars, Mikó, István, Bartolomaeus, Thomas
In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only
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PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature J. Biomed. Semant. (IF 1.6) Pub Date : 2022-06-11 Binkheder, Samar, Wu, Heng-Yi, Quinney, Sara K., Zhang, Shijun, Zitu, Md. Muntasir, Chiang, Chien‐Wei, Wang, Lei, Jones, Josette, Li, Lang
Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One
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Synthesizing evidence from clinical trials with dynamic interactive argument trees J. Biomed. Semant. (IF 1.6) Pub Date : 2022-06-03 Sanchez-Graillet, Olivia, Witte, Christian, Grimm, Frank, Grautoff, Steffen, Ell, Basil, Cimiano, Philipp
Evidence-based medicine propagates that medical/clinical decisions are made by taking into account high-quality evidence, most notably in the form of randomized clinical trials. Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach –by means of systematic reviews– a conclusive recommendation on which treatment is best suited for a given patient population
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Exploiting document graphs for inter sentence relation extraction J. Biomed. Semant. (IF 1.6) Pub Date : 2022-06-03 Le, Hoang-Quynh, Can, Duy-Cat, Collier, Nigel
Most previous relation extraction (RE) studies have focused on intra sentence relations and have ignored relations that span sentences, i.e. inter sentence relations. Such relations connect entities at the document level rather than as relational facts in a single sentence. Extracting facts that are expressed across sentences leads to some challenges and requires different approaches than those usually
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An annotated corpus of clinical trial publications supporting schema-based relational information extraction J. Biomed. Semant. (IF 1.6) Pub Date : 2022-05-23 Sanchez-Graillet, Olivia, Witte, Christian, Grimm, Frank, Cimiano, Philipp
The evidence-based medicine paradigm requires the ability to aggregate and compare outcomes of interventions across different trials. This can be facilitated and partially automatized by information extraction systems. In order to support the development of systems that can extract information from published clinical trials at a fine-grained and comprehensive level to populate a knowledge base, we
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SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks J. Biomed. Semant. (IF 1.6) Pub Date : 2022-05-08 Oliveira, Lucas Emanuel Silva e, Peters, Ana Carolina, da Silva, Adalniza Moura Pucca, Gebeluca, Caroline Pilatti, Gumiel, Yohan Bonescki, Cintho, Lilian Mie Mukai, Carvalho, Deborah Ribeiro, Al Hasan, Sadid, Moro, Claudia Maria Cabral
The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian
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Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic J. Biomed. Semant. (IF 1.6) Pub Date : 2022-04-25 Queralt-Rosinach, Núria, Kaliyaperumal, Rajaram, Bernabé, César H., Long, Qinqin, Joosten, Simone A., van der Wijk, Henk Jan, Flikkenschild, Erik L.A., Burger, Kees, Jacobsen, Annika, Mons, Barend, Roos, Marco
The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical
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Improving reusability along the data life cycle: a regulatory circuits case study J. Biomed. Semant. (IF 1.6) Pub Date : 2022-03-28 Louarn, Marine, Chatonnet, Fabrice, Garnier, Xavier, Fest, Thierry, Siegel, Anne, Faron, Catherine, Dameron, Olivier
In life sciences, there has been a long-standing effort of standardization and integration of reference datasets and databases. Despite these efforts, many studies data are provided using specific and non-standard formats. This hampers the capacity to reuse the studies data in other pipelines, the capacity to reuse the pipelines results in other studies, and the capacity to enrich the data with additional
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Defining health data elements under the HL7 development framework for metadata management J. Biomed. Semant. (IF 1.6) Pub Date : 2022-03-18 Yang, Zhe, Jiang, Kun, Lou, Miaomiao, Gong, Yang, Zhang, Lili, Liu, Jing, Bao, Xinyu, Liu, Danhong, Yang, Peng
Health data from different specialties or domains generallly have diverse formats and meanings, which can cause semantic communication barriers when these data are exchanged among heterogeneous systems. As such, this study is intended to develop a national health concept data model (HCDM) and develop a corresponding system to facilitate healthcare data standardization and centralized metadata management
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Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data J. Biomed. Semant. (IF 1.6) Pub Date : 2022-03-15 Kaliyaperumal, Rajaram, Wilkinson, Mark D., Moreno, Pablo Alarcón, Benis, Nirupama, Cornet, Ronald, dos Santos Vieira, Bruna, Dumontier, Michel, Bernabé, César Henrique, Jacobsen, Annika, Le Cornec, Clémence M. A., Godoy, Mario Prieto, Queralt-Rosinach, Núria, Schultze Kool, Leo J., Swertz, Morris A., van Damme, Philip, van der Velde, K. Joeri, Lalout, Nawel, Zhang, Shuxin, Roos, Marco
The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all
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Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma J. Biomed. Semant. (IF 1.6) Pub Date : 2022-02-23 Tariq, Amara, Kallas, Omar, Balthazar, Patricia, Lee, Scott Jeffery, Desser, Terry, Rubin, Daniel, Gichoya, Judy Wawira, Banerjee, Imon
Transfer learning is a common practice in image classification with deep learning where the available data is often limited for training a complex model with millions of parameters. However, transferring language models requires special attention since cross-domain vocabularies (e.g. between two different modalities MR and US) do not always overlap as the pixel intensity range overlaps mostly for images
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A multipurpose TNM stage ontology for cancer registries J. Biomed. Semant. (IF 1.6) Pub Date : 2022-02-22 Nicholson, Nicholas Charles, Giusti, Francesco, Bettio, Manola, Carvalho, Raquel Negrao, Dimitrova, Nadya, Dyba, Tadeusz, Flego, Manuela, Neamtiu, Luciana, Randi, Giorgia, Martos, Carmen
Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs
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Extending electronic medical records vector models with knowledge graphs to improve hospitalization prediction J. Biomed. Semant. (IF 1.6) Pub Date : 2022-02-22 Gazzotti, Raphaël, Faron, Catherine, Gandon, Fabien, Lacroix-Hugues, Virginie, Darmon, David
Artificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening their analysis and decisions, thereby improving the health of patients. On the one hand, machine learning algorithms have proven their effectiveness in extracting information and exploiting knowledge extracted from data. On the other hand, knowledge graphs capture
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Text mining-based measurement of precision of polysomnographic reports as basis for intervention J. Biomed. Semant. (IF 1.6) Pub Date : 2022-01-31 Baty, Florent, Hegermann, Jemima, Locatelli, Tiziana, Rüegg, Claudio, Gysin, Christian, Rassouli, Frank, Brutsche, Martin
Text mining can be applied to automate knowledge extraction from unstructured data included in medical reports and generate quality indicators applicable for medical documentation. The primary objective of this study was to apply text mining methodology for the analysis of polysomnographic medical reports in order to quantify sources of variation – here the diagnostic precision vs. the inter-rater