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Ontology-Aware Clinical Abstractive Summarization [article]

Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, Ross W. Filice
2019 arXiv   pre-print
We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation.  ...  Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors.  ...  Ontology-aware pointer-generator (Ontology PG).  ... 
arXiv:1905.05818v1 fatcat:6l2eam2zhbdxtg7wkllxccso4a

Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization [article]

Sajad Sotudeh and Nazli Goharian and Ross W. Filice
2020 arXiv   pre-print
In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer.  ...  Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task.  ...  Conclusion We proposed an approach to content selection for abstractive text summarization in clinical notes.  ... 
arXiv:2005.00163v1 fatcat:434xxvlutfhw3akzjubzi65oxm

An Ontology for Telemedicine Systems Resiliency to Technological Context Variations in Pervasive Healthcare

Nekane Larburu, Richard G. A. Bults, Marten J. Van Sinderen, Ing Widya, Hermie J. Hermens
2015 IEEE Journal of Translational Engineering in Health and Medicine  
This paper presents an ontology that specifies the relation among technological context, quality of clinical data, and patient treatment.  ...  The presented ontology provides a formal way to represent the knowledge to specify the effect of technological context variations in the clinical data quality and the impact of the clinical data quality  ...  This ontology is the result of applying a refined RE method and the layering technique from [4] , which is summarized here. A.  ... 
doi:10.1109/jtehm.2015.2458870 pmid:27170903 pmcid:PMC4848059 fatcat:ybp6onf5yne2lanuheriei4rre

Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization [article]

Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita
2022 arXiv   pre-print
In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization.  ...  While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical  ...  model consisting of a content selector and abstractive summarizer for clinical abstractive summarization.  ... 
arXiv:2204.00797v1 fatcat:ekwobbr4qvcz3f2vx22a3wptxm

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

Muhammad Razzaq, Claudia Villalonga, Sungyoung Lee, Usman Akhtar, Maqbool Ali, Eun-Soo Kim, Asad Khattak, Hyonwoo Seung, Taeho Hur, Jaehun Bang, Dohyeong Kim, Wajahat Ali Khan
2017 Sensors  
(i.e., physical activity, nutrition, and clinical) (see Section 3.2), (2) infer a more abstract representation of cross-domain contexts (see Section 5.1), (3) separate the ontology model (T-Box) and the  ...  ., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness.  ...  These overlapping contexts are vertically fused to obtain abstract context form, using ontological based reasoning.  ... 
doi:10.3390/s17102433 pmid:29064459 pmcid:PMC5677224 fatcat:cg6wooyl6rbb7hwzh4zxlprvui

Intelligent Agent Architecture for Context Aware Service

Mechelle Grace Zaragoza, Haeng-Kon Kim
2018 International Journal of Control and Automation  
The ultimate goal of this study is to develop an intelligent agent platform for the ontological context-based service and apply the intelligent agent platform developed to the health care application domain  ...  The "conceptualization 0" is an abstract model. "Formal" means that the computer must be able to understand the content of the ontology.  ...  However, most studies are limited to building a concrete knowledge base and presenting an abstract model without the "ontological design" inference rule.  ... 
doi:10.14257/ijca.2018.11.10.05 fatcat:z6kqr7bz6vcizogjtk6msnzhdu

A Clinical Context-aware Automated Summarization using Deep Neural Network: Model Development and Validation (Preprint)

Muhammad Afzal, Fakhare Alam, Khalid Mahmood Malik, Ghaus M. Malik
2020 JMIR Medical Informatics  
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.  ...  Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions.  ...  PQR: prognosis quality recognition; CCA: clinical context-aware; PICO: Population/Problem, Intervention, Comparison, Outcome.  ... 
doi:10.2196/19810 pmid:33095174 fatcat:gkfwickxdrfu5ntam4hrpdgwjq

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network

Christopher Ochs, Yehoshua Perl, James Geller, Melissa Haendel, Matthew Brush, Sivaram Arabandi, Samson Tu
2015 Journal of Biomedical Informatics  
In this paper, we introduce Diff Abstraction Networks ("Diff AbNs"), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology  ...  The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research,  ...  Abstraction networks An abstraction network (''AbN'') is a compact network that summarizes the knowledge in an ontology.  ... 
doi:10.1016/j.jbi.2015.05.018 pmid:26048076 pmcid:PMC4532611 fatcat:v6ewyvfolndojldseseyg3aluq

Automated methods for the summarization of electronic health records: Table 1

Rimma Pivovarov, Noémie Elhadad
2015 JAMIA Journal of the American Medical Informatics Association  
No Performs semantic, temporal, and context abstraction. Requires domain-specific ontologies. Consists of a knowledge base, abstraction generator, navigation engine, and visualization.  ...  knowledge and ontologies.  ... 
doi:10.1093/jamia/ocv032 pmid:25882031 pmcid:PMC4986665 fatcat:3p5ujo7gpjchzn6kiyvdkqrd4e

As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Jean Charlet, Ferdinand Dhombres
2018 IMIA Yearbook of Medical Informatics  
Conclusions: Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices.  ...  Objectives: To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM).  ...  and abstracts of the articles (for example "terminologies [TIAB]").  ... 
doi:10.1055/s-0038-1667078 pmid:30157517 fatcat:otwpqzgeang5to4l7xfpiaf6ra

Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review

Paolo Fraccaro, Mercedes Arguello Castelerio, John Ainsworth, Iain Buchan
2015 JMIR Medical Informatics  
The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth.  ...  Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines.  ...  Abidi [72] Controlled compar- ison-expert panel Generalist doc- tor/chronic cardio- vascular diseases Interactivity & summarization Knowledge-based sys- tem (ontology based)/desktop applica-  ... 
doi:10.2196/medinform.3503 pmid:25785897 pmcid:PMC4318680 fatcat:4e4sq3qcu5byxggi2pgh4yk4bm

Text Mining In Healthcare

 Text summarization for Clinical Research NLP have been used for various application in healthcare system.  ...  DOI: 10.35940/ijitee.B1111.1292S19 Table - - I is work done in text summarization for clinical research.  ... 
doi:10.35940/ijitee.b1111.1292s19 fatcat:s6rtt3a33ncrfgpxpksnu2hdny

Introducing Context-Awareness and Adaptation in Telemedicine Systems [chapter]

Charalampos Doukas, Ilias Maglogiannis, Kostas Karpouzis
2010 Studies in Computational Intelligence  
This chapter presents a platform for performing proper medical content adaptation based on context awareness.  ...  The paper discusses the design of the ontological model and provides an initial assessment.  ...  context-aware clinical computer systems.  ... 
doi:10.1007/978-3-642-11684-1_10 fatcat:uav2bwcp6bdv5eqivfbm3ft7z4

Comparing different knowledge sources for the automatic summarization of biomedical literature

Laura Plaza
2014 Journal of Biomedical Informatics  
the documents to summarize.  ...  Different graphs are created by using different combinations of ontologies and vocabularies within the UMLS (including GO, SNOMED-CT, HUGO and all available vocabularies in the UMLS) to retrieve domain  ...  Aware of this situation, the text summarization community is actively working toward the development of domain-specific methods that help manage this information overload.  ... 
doi:10.1016/j.jbi.2014.07.014 pmid:25066773 fatcat:hw6z3w64nrdfvaa2hysn5bvlqu

Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis

María Taboada, Diego Martínez, Belén Pilo, Adriano Jiménez-Escrig, Peter N Robinson, María J Sobrido
2012 BMC Medical Informatics and Decision Making  
at different levels of abstraction.  ...  This exchange involves mapping abstract phenotype descriptions from research resources, such as knowledge databases and catalogs, to unstructured datasets produced through experimental methods and clinical  ...  Still, if used with awareness of its limitations, this type of genotype-phenotype exploration will be of clinical utility.  ... 
doi:10.1186/1472-6947-12-78 pmid:22849591 pmcid:PMC3444309 fatcat:i6cop6ugivhepfd2bhjjffy2wa
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