Journal of Biomedical Semantics最新文献

{"title":"Prenatal monitoring in primary health care: a design science research-based approach to FHIR interoperability.","authors":"Renata D Braga, Plinio S Leitao-Junior, Suse B Castilho, Dandra A Souza, Lismary B Oliveira, Michael L D Oliveira, Luiselena L Esmeraldo, Carlos A G Tibiriçá, Diana F Guimarães, Aline R Nogueira, Livia M Vilela, Rejane F Ribeiro-Rotta, Fabio N Lucena, Juliana P Souza-Zinader","doi":"10.1186/s13326-026-00356-7","DOIUrl":"10.1186/s13326-026-00356-7","url":null,"abstract":"<p><strong>Background: </strong>This research addresses the development and validation of an information model for pregnancy monitoring that, through the Fast Healthcare Interoperability Resources (FHIR) specification, seeks to promote interoperability for the quality of prenatal care by multidisciplinary teams in Brazilian primary healthcare contexts. The Design Science Research (DSR) approach is applied to propose and evaluate the model, through quality strategies, where iterations increase the mode maturity in accordance with healthcare scenarios.</p><p><strong>Results: </strong>A set of use cases is constructed from user stories in primary care that abstract health concepts for pregnancy monitoring. The information model is presented in a hierarchical structure, with the categorization of related concepts grouped into pillars, in the context of the integration of health professionals who work in a complementary manner. The traceability of the use cases in relation to the semantic pillars of the information model, the implementation of the model from a FHIR perspective, and how the model complies with the ISO 13972:2022 standard (Health informatics - Clinical information models - Characteristics, structures and requirements) are analyzed.</p><p><strong>Conclusions: </strong>This work makes several key contributions, starting with the development of an information model for pregnancy monitoring. This model integrates different areas of primary care to promote a holistic and personalized approach to prenatal care. To ensure data can be shared effectively, it establishes health information interoperability using the HL7 FHIR R4 standard, which involved mapping resources and creating profiles as specified in an implementation guide. The study rigorously applies the Design Science Research method, utilizing multidisciplinary consensus strategies to scientifically improve and evolve the information model.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13471499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148015714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.","authors":"Sangjun Yea, Ho Jang, Jaeuk U Kim","doi":"10.1186/s13326-026-00355-8","DOIUrl":"10.1186/s13326-026-00355-8","url":null,"abstract":"<p><strong>Background: </strong>Traditional medicine (TM) employs medicinal herbs (MHs) to manage diverse health conditions, yet much of this evidence remains embedded in narrative biomedical text, limiting its use in computational analysis and decision making. As integrative medicine increasingly demands reliable, machine-interpretable knowledge, there is a need for structured representations of MH-disease relationships that prioritize evidential clarity and semantic consistency. This study aims to develop an expert-annotated corpus designed to support decision-relevant knowledge extraction for integrative biomedical informatics.</p><p><strong>Methods: </strong>We developed the Medicinal Herb-Disease Relationships (MHDR) corpus by systematically collecting 800 PubMed abstracts using standardized pharmacognostic names. To ensure reliability for decision-support applications, relationships were extracted exclusively from key sentences-sentences that explicitly and unambiguously state MH-disease associations and provide direct evidence for the underlying claims. Relationships expressed in non-key sentences were intentionally excluded, as they often contain implicit, speculative, or context-dependent statements that may reduce decision reliability. Three TM experts manually annotated MH entities, disease entities, and explicit relationships through a four-phase consensus-driven protocol. Baseline Transformer-based models were evaluated for entity recognition, key-sentence identification, and relation extraction to assess the corpus's computational usability.</p><p><strong>Results: </strong>The MHDR corpus contains 5,119 medicinal herb mentions, 6,621 disease mentions, and 1,314 high-confidence MH-disease relationships derived from 832 decision-relevant key sentences. Baseline Transformer models demonstrated robust performance in recognizing entities and extracting relationships, confirming that the corpus supports stable and interpretable knowledge extraction suitable for downstream decision-support and evidence-integration tasks.</p><p><strong>Conclusion: </strong>The MHDR corpus represents a decision-oriented informatics resource for modeling medicinal herb-disease knowledge in TM. By restricting annotations to explicit evidence-bearing sentences, the corpus enhances semantic reliability and supports computable reasoning, enabling its use in clinical research analytics, ontology alignment, and integrative decision-making systems bridging traditional and modern medicine. The MHDR corpus is publicly available through GitHub ( https://github.com/KIOM-AIDoc/MHDR ) and Figshare ( https://doi.org/10.6084/m9.figshare.29555549 ).</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13352773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147929858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BERTopic-driven term extraction from biomedical texts toward ontology population: evaluating vaccine ontology with Plotkin's vaccines corpus.","authors":"B Damayanthi Jesudas, Sam Smith, Feng-Yu Yeh, Jie Zheng, John Beverley, William D Duncan, Yongqun He","doi":"10.1186/s13326-026-00353-w","DOIUrl":"10.1186/s13326-026-00353-w","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Ontologies are essential for structuring biomedical knowledge, supporting semantic integration, reasoning, and data interoperability. In vaccinology, ontology population is particularly critical, as vaccines span diverse domains. A well-defined Vaccine Ontology enables consistent knowledge representation, integration across datasets, and supports applications such as decision support, literature mining, and semantic search. However, manual ontology population is tedious, time-consuming, and difficult to maintain in this dynamically evolving domain, underscoring the need for automated or semi-automated population approaches.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We present a semi-automated pipeline that uses BERTopic, an advanced topic modeling approach that leverages Bidirectional Encoder Representations from Transformers (BERT)-based embeddings to extract ontology relevant concepts from biomedical text. To evaluate the effectiveness of this automated approach, the method is applied to Plotkin's Vaccines corpus, a leading reference text in vaccinology that synthesizes scientific, clinical, and policy perspectives on vaccines. The workflow integrates multiple natural language processing (NLP) components: document preprocessing with spaCy part-of-speech tagging and vectorization, sentence embeddings generated by a lightweight transformer model (all-MiniLM-L6-v2), dimensionality reduction with Uniform Manifold Approximation and Projection (UMAP), clustering with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and topic representation. To guide topic discovery toward vaccine-relevant concepts and filter irrelevant terms, the pipeline incorporates a curated set of vaccine-focused terms derived from the existing Vaccine Ontology (VO) as seed words to influence topic representations, while preserving the unsupervised nature of the clustering process. To enhance interpretability, the pipeline employs Keyword extraction using BERT embeddings (KeyBERT) for automatic keyword-based labeling, supplemented with disambiguated descriptive labels, and Bidirectional and Auto-Regressive Transformer (BART) summarization for topic-level summaries. The resulting hierarchical topic structures are further refined through a tree-merging module that unifies multiple topic hierarchies into a coherent ontology-like representation. The extracted topics are reviewed by the Subject Matter Experts (SMEs) to filter irrelevant terms and then mapped to Vaccine Ontology, a well-established ontology to assess their relevance and coverage, demonstrating how automated methods can reduce the labor-intensive effort required for manual ontology population.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The script is customized to generate a varying number of topics and keywords. In this study, the top 50 topics with 10 keywords per topic were extracted for each chapter of Plotkin's vaccines. The pipeline produced coherent topic clusters repr","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13599076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147815427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Representing dental caries and dysbiosis within the oral microbiome in the Oral Health and Disease Ontology.","authors":"William D Duncan, Amarpreet Sabharwal, Alexander D Diehl, Nivedita Dutta, Matthew Diller, Marcin P Joachimiak, Gopikrishnan M Chandrasekharan","doi":"10.1186/s13326-026-00350-z","DOIUrl":"10.1186/s13326-026-00350-z","url":null,"abstract":"<p><p>BACKGROUND: Dental caries is an oral health condition in which cariogenic bacteria demineralize and decay teeth. It arises due to interaction between the host, environment, and oral microbiome. Current terminologies and ontologies, however, do not accurately represent the important role that the microbiome has in the formation of carious lesions. Rather, they focus on the anatomical features of carious lesions and often obfuscate the distinctions between dental caries as a disease affecting a tooth, as lesions that are produced because of the disease, and as lesions produced as a result of dysbiosis in the oral microbiome. To capture the current state of evidence and provide flexibility for evolving literature on host-environment-microbiome interactions, there is a need to revise and expand the ontological framework for dental caries. RESULTS: Several established terminologies and ontologies were reviewed for terms used to represent dental caries and the oral microbiome. We found that they either did not represent or misrepresented the current scientific understanding of caries and its relation to the microbial dysbiosis. As a result of these deficiencies, we added terms and relations to the Oral Health and Disease Ontology (OHD) that more accurately represent how oral microbial dysbiosis influences the development of dental caries. CONCLUSIONS: The Oral Health and Disease Ontology is an advance over existing ontologies for representing the impact of oral microbial dysbiosis on dental caries. It provides a semantic framework that better serves the needs of cariology researchers and can more easily incorporate new oral microbiome findings. </p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"17 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ClarID: a human-readable and compact identifier specification for biomedical metadata integration.","authors":"Manuel Rueda, Ivo G Gut","doi":"10.1186/s13326-026-00349-6","DOIUrl":"10.1186/s13326-026-00349-6","url":null,"abstract":"<p><p>BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., ‘CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W’ that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (relative to that event); and (ii) a compact version named ‘stub’ (e.g., ‘CT01001LTR0N401T1W’) optimized for filenames, pipelines, and labeling. ClarID is supported by an open-source reference implementation, ClarID-Tools, a command-line tool that processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID’s utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID is designed to complement, not replace, persistent identifiers such as UUIDs, by providing a human-readable layer that enhances interpretability and facilitates metadata curation. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools . </p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"17 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13123180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Representing dental restoration materials in the oral health and disease ontology.","authors":"Nivedita Dutta, Michael DeBellis, Nripen Chanda, Alexander D Diehl, Finn Wilson, Mateus Rocha, Mattew Diller, Gopikrishnan M Chandrasekharan, William D Duncan","doi":"10.1186/s13326-026-00352-x","DOIUrl":"10.1186/s13326-026-00352-x","url":null,"abstract":"<p><p>BACKGROUND: Semantic clarity and standardization in representing dental restoration materials are essential for ensuring interoperability across research and clinical settings. However, existing ontologies, including those in the Open Biological and Biomedical Ontology (OBO) Foundry, provide minimal coverage of this clinically relevant domain. METHODS: Guided by OBO Foundry principles, developed an ontological extension of the Oral Health and Disease (OHD) ontology to formally represent dental restoration materials. The modeling process included iterative expert input to ensure clinical accuracy and precise terminology. Logical definitions and subclass hierarchies were created to classify materials by composition and microstructure. RESULTS: The extended ontology introduces a logically structured taxonomy of dental restoration materials using genus-differentia definitions. Its class hierarchy aligns with domain-specific classification systems and captures current and emerging material types. Reuse of class from the Chemical Entities of Biological Interest (ChEBI) and the Environment Ontology (ENVO) as well as relations from the Relation Ontology (RO) supports semantic integration with related biomedical ontologies. CONCLUSION: This work highlights how domain-informed ontology design can effectively capture complex material knowledge. The extensive collaboration integrates expertise from ontology engineers, academic researchers, and practicing clinicians. This approach enables the capture of intricate, often-disregarded real clinical factors, resulting in a model that reflects both the structural properties of materials and their practical use. The extended OHD ontology improves the semantic representation of dental restoration materials and provides a scalable foundation for advancing oral health informatics. </p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"17 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13088783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge graph embedding and alignment of incomplete electronic health records for critical care applications.","authors":"Shervin Mehryar, Michel Dumontier","doi":"10.1186/s13326-026-00351-y","DOIUrl":"10.1186/s13326-026-00351-y","url":null,"abstract":"<p><p>BACKGROUND: Data-driven AI models in clinical care increasingly rely on integrating data from heterogeneous sources to improve predictive performance. Traditional Electronic Health Record (EHR) systems however pose significant challenges for data integration due to the use of diverse standards and inconsistent semantics. Personalized Health Knowledge Graphs (PHKG), which leverage biomedical ontologies to encode and interconnect clinical knowledge, have emerged as a promising solution for unifying heterogeneous health data. Yet the process of PHKG integration can result in incomplete and ambiguous representations. OBJECTIVE: Our primary objective is to apply advanced embedding techniques to mitigate the incompleteness problem in PHKGs. We propose a framework that combines schema-based semantic integration, domain-specific ontology alignment, and patient context representations through embedding techniques to improve incomplete and ambiguous PHKG representations. We further demonstrate the framework’s capabilities for producing enriched representations in a use case concerning cardiovascular outcome prediction by training machine learning models which utilize the learned embeddings. RESULTS: We embed EHR from a critical care unit as structured PHKGs mapped with two different schemas for comparison across knowledge completion and alignment tasks following a modular design. Each module is individually optimized using three baseline embedding methods with enhanced loss functions. We evaluate each task for both accuracy and semantic consistency and show the contribution of each module to the overall performance. We find and report settings for each module in which the proposed framework outperforms baseline results. The learned representations are subsequently used to generate patient contexts for the task of Heart Failure diagnosis as a use case. Our experiments demonstrate that semantically enhanced PHKG embeddings achieve better precision and recall scores compared to baseline models. CONCLUSION: Our proposed method addresses the challenges in generating heterogenuous Personalized Health Knowledge Graphs (PHKG) through a modular framework that integrates schema mapping, ontology alignment, and contextual patient embeddings. Results on real-world patient records support the potential for improved performance in clinical decision making. Particularly in scenarios where sparse and fragmented health records prove problematic for data driven applications, our method can provide a robust approach for the disambiguation and completion of coded information. Our implementation is available at: https://github.com/AIDAVA-DEV/kge-framework . </p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13227801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147673612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing the bioassay ontology through integrated PK/PD and safety pharmacology representation.","authors":"Joan Glenny-Pescov, Caty Chung, Nicolette Ross, Jiaming Hu, Michael Sinclair, Rabia Khurshid, Anneli Karlsson, Stephan C Schürer","doi":"10.1186/s13326-025-00342-5","DOIUrl":"10.1186/s13326-025-00342-5","url":null,"abstract":"<p><p>The latest release of the BioAssay Ontology (BAO), version 2.8.16, introduces major updates that expand its ability to describe and categorize assays related to pharmacokinetics, pharmacodynamics, and safety pharmacology. These refinements, driven by collaboration with the Semantic Enrichment of Electronic Laboratory Notebook Data project, an industry initiative led by the Pistoia Alliance, address previously identified gaps in ontology coverage. New terms were added for disease models, preclinical study parameters, toxicological measurements, and detailed classifications of pharmacokinetics and pharmacodynamics assays. The update also incorporates biologically relevant target classes, including cytochrome P450 enzymes, solute carrier transporters, and uridine diphosphate-glucuronosyltransferase enzymes. Beyond content expansion, structural improvements include reassignment of terms to more specific and semantically appropriate parent classes, refinement of class dependencies, and enhanced alignment with external ontologies. Anatomical terms were reorganized to follow the Uber Anatomy Ontology hierarchy, and new chemical classes were incorporated to improve compatibility with the Chemical Entities of Biological Interest Ontology. Hundreds of additional axioms were added using existing object properties to capture assay formats, endpoints, detection methods, substrates, design strategies, and biological context. These refinements improve BAO’s semantic precision, interoperability, and reasoning capabilities. As a demonstration of these capabilities, we present a reasoning-based use case in which BAO’s equivalent class axioms enable automated classification of passive cell permeability and active efflux substrate assays. The ontology infers broader mechanistic categories from shared modeled characteristics, grouping transporter-specific and cell-based permeability assays under their mechanistic parents, while excluding assays that do not meet all restrictions. This example demonstrates its ability to support precise, inference-driven retrieval and integration of assay classes. By extending its scope and improving the clarity, consistency, and semantic depth of its classifications, BAO continues to serve as a vital resource for organizing pharmacological data and advancing research in both academic and industrial settings.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"17 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12983555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical and nuanced framework for entity linking evaluation.","authors":"Fuqi Xu, Goran Nenadic, Robert Stevens","doi":"10.1186/s13326-025-00339-0","DOIUrl":"10.1186/s13326-025-00339-0","url":null,"abstract":"<p><p>BACKGROUND: Entity linking maps textual mentions with entities in vocabularies. While accuracy is the primary metric for entity linking evaluation, it fails to capture the complexity of model behaviour. RESULTS: We propose an entity linking evaluation framework that clarifies the target objects of metrics calculation, incorporates term hierarchy, generates performance profiles, and summarises them as model characteristics. The framework emphasises hierarchical vocabulary structures, shifting the focus from text-level label matching to semantic comparison between concepts. We illustrate the competence and utility of this framework through a case study on disease entity linking. CONCLUSION: Our results highlight the importance of aligning evaluation metrics with application-specific requirements, and provide structured hierarchical error analysis for entity linking, paving the way for more nuanced and practical assessments of entity linking systems. </p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12967008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146180025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An application-based ontological knowledge base of medications to support health literacy and adherence for the consumer population: an aging population use case.","authors":"Clifford Chen, Muhammad Amith, Kirk Roberts, Rebecca Mauldin, Renata Komalasari, Cui Tao","doi":"10.1186/s13326-026-00347-8","DOIUrl":"10.1186/s13326-026-00347-8","url":null,"abstract":"<p><p>BACKGROUND: The geriatric population is a vulnerable population that has higher rate of chronic disease and represents the largest portion of healthcare delivered. This population is vulnerable to medication non-adherence. Patient medication non-adherence is a problem that can lead to increase morbidity and mortality and waste of resources. Reasons for this phenomenon are multifactorial and include poor health literacy. RESULTS: To address this issue, we created a patient-directed drug information knowledge graph using both patient-directed resources and the Vaccine Information Statement Ontology (VISO), and attempt to validate the knowledge graph with common patient questions. This knowledge graph model (Patient-centric Drug Knowledge Graph) includes a terminological size of 577 term nodes and 113 links. We also created five knowledge graphs using the Patient-centric Drug Knowledge Graph (PcDKG) framework that represent five top medications (atorvastatin, levothyroxine, lisinopril, metformin, and amlodipine) used by the geriatric population. The common patient questions were converted to SPARQL queries to assess the coverage the model. CONCLUSION: This initial development of the PcDKG is first knowledge graph that synthesizes concepts relating to drug information needs of the consumer population, specifically the geriatric population. This work also evolves the previous work of the VISO knowledge graph to cover wider range of medication knowledge for patients. PcDKG aims to be integrated in patient-directed tools to leverage its knowledge base, and future direction is to integrate PcDKG for digital health tools directed to the geriatric population. Our work is publicly available on our GitHub repository along with the five instance knowledge graph models. </p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12924406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}