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https://www.readbyqxmd.com/read/28819351/a-systematic-analysis-of-term-reuse-and-term-overlap-across-biomedical-ontologies
#1
Maulik R Kamdar, Tania Tudorache, Mark A Musen
Reusing ontologies and their terms is a principle and best practice that most ontology development methodologies strongly encourage. Reuse comes with the promise to support the semantic interoperability and to reduce engineering costs. In this paper, we present a descriptive study of the current extent of term reuse and overlap among biomedical ontologies. We use the corpus of biomedical ontologies stored in the BioPortal repository, and analyze different types of reuse and overlap constructs. While we find an approximate term overlap between 25-31%, the term reuse is only <9%, with most ontologies reusing fewer than 5% of their terms from a small set of popular ontologies...
2017: Semantic Web
https://www.readbyqxmd.com/read/28791315/retracted-generating-personalized-web-search-using-semantic-context
#2
The Scientific World Journal
[This retracts the article DOI: 10.1155/2015/462782.].
2017: TheScientificWorldJournal
https://www.readbyqxmd.com/read/28766103/protocol-driven-decision-support-within-e-referral-systems-to-streamline-patient-consultation-triaging-and-referrals-from-primary-care-to-specialist-clinics
#3
Ehsan Maghsoud-Lou, Sean Christie, Samina Raza Abidi, Syed Sibte Raza Abidi
Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process...
September 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28764813/an-ontology-driven-tool-for-structured-data-acquisition-using-web-forms
#4
Rafael S Gonçalves, Samson W Tu, Csongor I Nyulas, Michael J Tierney, Mark A Musen
BACKGROUND: Structured data acquisition is a common task that is widely performed in biomedicine. However, current solutions for this task are far from providing a means to structure data in such a way that it can be automatically employed in decision making (e.g., in our example application domain of clinical functional assessment, for determining eligibility for disability benefits) based on conclusions derived from acquired data (e.g., assessment of impaired motor function). To use data in these settings, we need it structured in a way that can be exploited by automated reasoning systems, for instance, in the Web Ontology Language (OWL); the de facto ontology language for the Web...
August 1, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28750030/minimally-inconsistent-reasoning-in-semantic-web
#5
Xiaowang Zhang
Reasoning with inconsistencies is an important issue for Semantic Web as imperfect information is unavoidable in real applications. For this, different paraconsistent approaches, due to their capacity to draw as nontrivial conclusions by tolerating inconsistencies, have been proposed to reason with inconsistent description logic knowledge bases. However, existing paraconsistent approaches are often criticized for being too skeptical. To this end, this paper presents a non-monotonic paraconsistent version of description logic reasoning, called minimally inconsistent reasoning, where inconsistencies tolerated in the reasoning are minimized so that more reasonable conclusions can be inferred...
2017: PloS One
https://www.readbyqxmd.com/read/28679928/semantic-web-reusable-learning-objects-personal-learning-networks-in-health-key-pieces-for-digital-health-literacy
#6
Stathis Th Konstantinidis, Heather Wharrad, Richard Windle, Panagiotis D Bamidis
The knowledge existing in the World Wide Web is exponentially expanding, while continuous advancements in health sciences contribute to the creation of new knowledge. There are a lot of efforts trying to identify how the social connectivity can endorse patients' empowerment, while other studies look at the identification and the quality of online materials. However, emphasis has not been put on the big picture of connecting the existing resources with the patients "new habits" of learning through their own Personal Learning Networks...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28679487/issues-associated-with-the-use-of-semantic-web-technology-in-knowledge-acquisition-for-clinical-decision-support-systems-systematic-review-of-the-literature
#7
Seyedjamal Zolhavarieh, David Parry, Quan Bai
BACKGROUND: Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the "best" knowledge to use...
July 5, 2017: JMIR Medical Informatics
https://www.readbyqxmd.com/read/28650813/knowledge-based-topic-model-for-unsupervised-object-discovery-and-localization
#8
Zhenxing Niu, Gang Hua, Le Wang, Xinbo Gao
Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models such as Latent Dirichlet Allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery...
June 22, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641261/robust-web-image-annotation-via-exploring-multi-facet-and-structural-knowledge
#9
Mengqiu Hu, Yang Yang, Fumin Shen, Luming Zhang, Heng Tao Shen, Xuelong Li
Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval, and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods, heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power)...
October 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28583204/building-a-semantic-web-based-metadata-repository-for-facilitating-detailed-clinical-modeling-in-cancer-genome-studies
#10
Deepak K Sharma, Harold R Solbrig, Cui Tao, Chunhua Weng, Christopher G Chute, Guoqian Jiang
BACKGROUND: Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains...
June 5, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28549446/-gnparser-a-powerful-parser-for-scientific-names-based-on-parsing-expression-grammar
#11
Dmitry Y Mozzherin, Alexander A Myltsev, David J Patterson
BACKGROUND: Scientific names in biology act as universal links. They allow us to cross-reference information about organisms globally. However variations in spelling of scientific names greatly diminish their ability to interconnect data. Such variations may include abbreviations, annotations, misspellings, etc. Authorship is a part of a scientific name and may also differ significantly. To match all possible variations of a name we need to divide them into their elements and classify each element according to its role...
May 26, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28479569/a-web-based-tool-to-enhance-monitoring-and-retention-in-care-for-tuberculosis-affected-patients
#12
Barbara Giannini, Niccolò Riccardi, Antonio Di Biagio, Giovanni Cenderello, Mauro Giacomini
Tuberculosis (TB) is responsible for a global epidemic. TB treatment requires long-term therapy usually with multiple drugs, which have potential side effects and interactions that may influence patients' adherence to treatment. The TB Ge network is a multi-centric web based platform that collects clinical information of TB affected patients to increase their support and retention in care. The system stores the list of all tuberculosis episodes for each patient with the related data, starting from the first visit including follow-ups clinical evaluations, laboratory tests, imaging and therapies...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28449114/neuro-symbolic-representation-learning-on-biological-knowledge-graphs
#13
Mona Alshahrani, Mohammed Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf
Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs...
April 25, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28423861/a-digital-framework-to-support-providers-and-patients-in-diabetes-related-behavior-modification
#14
Samina Abidi, Michael Vallis, Helena Piccinini-Vallis, Syed Ali Imran, Syed Sibte Raza Abidi
We present Diabetes Web-Centric Information and Support Environment (D-WISE) that features: (a) Decision support tool to assist family physicians to administer Behavior Modification (BM) strategies to patients; and (b) Patient BM application that offers BM strategies and motivational interventions to engage patients. We take a knowledge management approach, using semantic web technologies, to model the social cognition theory constructs, Canadian diabetes guidelines and BM protocols used locally, in terms of a BM ontology that drives the BM decision support to physicians and BM strategy adherence monitoring and messaging to patients...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423843/an-approach-for-the-support-of-semantic-workflows-in-electronic-health-records
#15
Marco Schweitzer, Alexander Hoerbst
With the unprecedented increase of healthcare data, technologies need to be both, highly efficient for the meaningful utilization of accessible data and flexible to adapt to future challenges and individual preferences. The OntoHealth system makes use of semantic technologies to enable flexible and individual interaction with Electronic Health Records (EHR) for physicians. This is achieved by the execution of formally modelled clinical workflows and the composition of Semantic Web Services (SWS). Several seamless components provide a service-oriented structure defined by individual designed EHR-workflows...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423840/discovering-central-practitioners-in-a-medical-discussion-forum-using-semantic-web-analytics
#16
Enayat Rajabi, Syed Sibte Raza Abidi
The aim of this paper is to investigate semantic web based methods to enrich and transform a medical discussion forum in order to perform semantics-driven social network analysis. We use the centrality measures as well as semantic similarity metrics to identify the most influential practitioners within a discussion forum. The centrality results of our approach are in line with centrality measures produced by traditional SNA methods, thus validating the applicability of semantic web based methods for SNA, particularly for analyzing social networks for specialized discussion forums...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423839/a-semantic-framework-for-logical-cross-validation-evaluation-and-impact-analyses-of-population-health-interventions
#17
Arash Shaban-Nejad, Anya Okhmatovskaia, Eun Kyong Shin, Robert L Davis, Brandi E Franklin, David L Buckeridge
Most chronic diseases are a result of a complex web of causative and correlated factors. As a result, effective public health or clinical interventions that intend to generate a sustainable change in these diseases most often use a combination of strategies or programs. To optimize comparative effectiveness evaluations and select the most efficient intervention(s), stakeholders (i.e. public health institutions, policy-makers and advocacy groups, practitioners, insurers, clinicians, and researchers) need access to reliable assessment methods...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423769/linked-data-applications-through-ontology-based-data-access-in-clinical-research
#18
Ann-Kristin Kock-Schoppenhauer, Christian Kamann, Hannes Ulrich, Petra Duhm-Harbeck, Josef Ingenerf
Clinical care and research data are widely dispersed in isolated systems based on heterogeneous data models. Biomedicine predominantly makes use of connected datasets based on the Semantic Web paradigm. Initiatives like Bio2RDF created Resource Description Framework (RDF) versions of Omics resources, enabling sophisticated Linked Data applications. In contrast, electronic healthcare records (EHR) data are generated and processed in diverse clinical subsystems within hospital information systems (HIS). Usually, each of them utilizes a relational database system with a different proprietary schema...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28419324/improving-data-workflow-systems-with-cloud-services-and-use-of-open-data-for-bioinformatics-research
#19
Md Rezaul Karim, Audrey Michel, Achille Zappa, Pavel Baranov, Ratnesh Sahay, Dietrich Rebholz-Schuhmann
Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted...
April 16, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/28408601/semantics-derived-automatically-from-language-corpora-contain-human-like-biases
#20
Aylin Caliskan, Joanna J Bryson, Arvind Narayanan
Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names...
April 14, 2017: Science
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