Retracted: Generating Personalized Web Search Using Semantic Context.

ScientificWorldJournal. 2017;2017:1295378

Authors: The Scientific World Journal

Abstract
[This retracts the article DOI: 10.1155/2015/462782.].

PMID: 28791315 [PubMed - in process]

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda.

Comput Support Coop Work. 2017 Feb-Mar;2017:1812-1834

Authors: Priedhorsky R, Osthus D, Daughton AR, Moran KR, Generous N, Fairchild G, Deshpande A, Del Valle SY

Abstract
Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

PMID: 28782059 [PubMed - in process]

Protocol-Driven Decision Support within e-Referral Systems to Streamline Patient Consultation, Triaging and Referrals from Primary Care to Specialist Clinics.

J Med Syst. 2017 Sep;41(9):139

Authors: Maghsoud-Lou E, Christie S, Abidi SR, Abidi SSR

Abstract
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. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.

PMID: 28766103 [PubMed - in process]

Minimally inconsistent reasoning in Semantic Web.

PLoS One. 2017;12(7):e0181056

Authors: Zhang X

Abstract
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. Some desirable properties are studied, which shows that the new semantics inherits advantages of both non-monotonic reasoning and paraconsistent reasoning. A complete and sound tableau-based algorithm, called multi-valued tableaux, is developed to capture the minimally inconsistent reasoning. In fact, the tableaux algorithm is designed, as a framework for multi-valued DL, to allow for different underlying paraconsistent semantics, with the mere difference in the clash conditions. Finally, the complexity of minimally inconsistent description logic reasoning is shown on the same level as the (classical) description logic reasoning.

PMID: 28750030 [PubMed - in process]

Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

Stud Health Technol Inform. 2017;238:219-222

Authors: Konstantinidis ST, Wharrad H, Windle R, Bamidis PD

Abstract
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. In this paper we propose a framework for empowering patients' digital health literacy adjusted to patients' currents needs by utilizing the contemporary way of learning through Personal Learning Networks, existing high quality learning resources and semantics technologies for interconnecting knowledge pieces. The framework based on the concept of knowledge maps for health as defined in this paper. Health Digital Literacy needs definitely further enhancement and the use of the proposed concept might lead to useful tools which enable use of understandable health trusted resources tailored to each person needs.

PMID: 28679928 [PubMed - in process]

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature.

JMIR Med Inform. 2017 Jul 05;5(3):e18

Authors: Zolhavarieh S, Parry D, Bai Q

Abstract
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. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.
OBJECTIVE: The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.
METHODS: We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.
RESULTS: The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.
CONCLUSIONS: The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.

PMID: 28679487 [PubMed - in process]

CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project.

Pharmacoepidemiol Drug Saf. 2017 Jun 28;:

Authors: Becker BFH, Avillach P, Romio S, van Mulligen EM, Weibel D, Sturkenboom MCJM, Kors JA, ADVANCE consortium

Abstract
BACKGROUND: Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts in the harmonization of codes as different vocabularies are being used across countries. In this paper, we present a web application called CodeMapper, which assists in the mapping of case definitions to codes from different vocabularies, while keeping a transparent record of the complete mapping process.
METHODS: CodeMapper builds upon coding vocabularies contained in the Metathesaurus of the Unified Medical Language System. The mapping approach consists of three phases. First, medical concepts are automatically identified in a free-text case definition. Second, the user revises the set of medical concepts by adding or removing concepts, or expanding them to related concepts that are more general or more specific. Finally, the selected concepts are projected to codes from the targeted coding vocabularies. We evaluated the application by comparing codes that were automatically generated from case definitions by applying CodeMapper's concept identification and successive concept expansion, with reference codes that were manually created in a previous epidemiological study.
RESULTS: Automated concept identification alone had a sensitivity of 0.246 and positive predictive value (PPV) of 0.420 for reproducing the reference codes. Three successive steps of concept expansion increased sensitivity to 0.953 and PPV to 0.616.
CONCLUSIONS: Automatic concept identification in the case definition alone was insufficient to reproduce the reference codes, but CodeMapper's operations for concept expansion provide an effective, efficient, and transparent way for reproducing the reference codes.

PMID: 28657162 [PubMed - as supplied by publisher]

Knowledge Based Topic Model for Unsupervised Object Discovery and Localization.

IEEE Trans Image Process. 2017 Jun 22;:

Authors: Niu Z, Hua G, Wang L, Gao X

Abstract
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. In this paper, prior knowledge in terms of the so-called Must-Links is exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called Latent Dirichlet Allocation with Mixture of Dirichlet Trees (LDA-MDT), is proposed to incorporate the Must-Links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the Must-Link is re-defined as that one Must-Link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the Must-Links are built and grouped with respect to specific object classes, thus the Must-Links in our approach are semantic-specific, which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several datasets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared to discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.

PMID: 28650813 [PubMed - as supplied by publisher]

Robust Web Image Annotation via Exploring Multi-facet and Structural Knowledge.

IEEE Trans Image Process. 2017 Jun 19;:

Authors: Hu M, Yang Y, Shen F, Zhang L, Shen HT, Li X

Abstract
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). In this paper, inspired by the promising advance of feature engineering (e.g., CNN feature and SIFT feature) and inexhaustible image data (associated with noisy and incomplete labels) on the Web, we propose an effective and robust scheme, termed Robust Multi-view Semi-supervised Learning (RMSL), for facilitating image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e. multiple views or features). We devise a robust pair-wise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via ℓ2,p loss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in RMSL. We conduct comprehensive experiments on three different datasets, and the results illustrate that our proposed approach is promising for automatic image annotation.

PMID: 28641261 [PubMed - as supplied by publisher]

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

J Biomed Semantics. 2017 Jun 07;8(1):21

Authors: Martínez-Romero M, Jonquet C, O'Connor MJ, Graybeal J, Pazos A, Musen MA

Abstract
BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms.
METHODS: We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data.
RESULTS: Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios.
CONCLUSIONS: Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).

PMID: 28592275 [PubMed - in process]

Building a semantic web-based metadata repository for facilitating detailed clinical modeling in cancer genome studies.

J Biomed Semantics. 2017 Jun 05;8(1):19

Authors: Sharma DK, Solbrig HR, Tao C, Weng C, Chute CG, Jiang G

Abstract
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.
METHODS: We leveraged a Semantic Web-based metadata repository enhanced with both ISO11179 metadata standard and Clinical Information Modeling Initiative (CIMI) Reference Model. We used the common data elements (CDEs) defined in The Cancer Genome Atlas (TCGA) data dictionary, and extracted the metadata of the CDEs using the NCI Cancer Data Standards Repository (caDSR) CDE dataset rendered in the Resource Description Framework (RDF). The ITEM/ITEM_GROUP pattern defined in the latest CIMI Reference Model is used to represent reusable model elements (mini-Archetypes).
RESULTS: We produced a metadata repository with 38 clinical cancer genome study domains, comprising a rich collection of mini-Archetype pattern instances. We performed a case study of the domain "clinical pharmaceutical" in the TCGA data dictionary and demonstrated enriched data elements in the metadata repository are very useful in support of building detailed clinical models.
CONCLUSION: Our informatics approach leveraging Semantic Web technologies provides an effective way to build a CIMI-compliant metadata repository that would facilitate the detailed clinical modeling to support use cases beyond TCGA in clinical cancer study domains.

PMID: 28583204 [PubMed - in process]

Increased in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis.

Int J Drug Policy. 2017 Jun 01;44:121-129

Authors: Lamy FR, Daniulaityte R, Nahhas RW, Barratt MJ, Smith AG, Sheth A, Martins SS, Boyer EW, Carlson RG

Abstract
BACKGROUND: Synthetic Cannabinoid Receptor Agonists (SCRA), also known as "K2" or "Spice," have drawn considerable attention due to their potential of abuse and harmful consequences. More research is needed to understand user experiences of SCRA-related effects. We use semi-automated information processing techniques through eDrugTrends platform to examine SCRA-related effects and their variations through a longitudinal content analysis of web-forum data.
METHOD: English language posts from three drug-focused web-forums were extracted and analyzed between January 1st 2008 and September 30th 2015. Search terms are based on the Drug Use Ontology (DAO) created for this study (189 SCRA-related and 501 effect-related terms). EDrugTrends NLP-based text processing tools were used to extract posts mentioning SCRA and their effects. Generalized linear regression was used to fit restricted cubic spline functions of time to test whether the proportion of drug-related posts that mention SCRA (and no other drug) and the proportion of these "SCRA-only" posts that mention SCRA effects have changed over time, with an adjustment for multiple testing.
RESULTS: 19,052 SCRA-related posts (Bluelight (n=2782), Forum A (n=3882), and Forum B (n=12,388)) posted by 2543 international users were extracted. The most frequently mentioned effects were "getting high" (44.0%), "hallucinations" (10.8%), and "anxiety" (10.2%). The frequency of SCRA-only posts declined steadily over the study period. The proportions of SCRA-only posts mentioning positive effects (e.g., "High" and "Euphoria") steadily decreased, while the proportions of SCRA-only posts mentioning negative effects (e.g., "Anxiety," 'Nausea," "Overdose") increased over the same period.
CONCLUSION: This study's findings indicate that the proportion of negative effects mentioned in web forum posts and linked to SCRA has increased over time, suggesting that recent generations of SCRA generate more harms. This is also one of the first studies to conduct automated content analysis of web forum data related to illicit drug use.

PMID: 28578250 [PubMed - as supplied by publisher]

"gnparser": a powerful parser for scientific names based on Parsing Expression Grammar.

BMC Bioinformatics. 2017 May 26;18(1):279

Authors: Mozzherin DY, Myltsev AA, Patterson DJ

Abstract
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. We refer to this as 'parsing' the name. Parsing categorizes name's elements into those that are stable and those that are prone to change. Names are matched first by combining them according to their stable elements. Matches are then refined by examining their varying elements. This two stage process dramatically improves the number and quality of matches. It is especially useful for the automatic data exchange within the context of "Big Data" in biology.
RESULTS: We introduce Global Names Parser (gnparser). It is a Java tool written in Scala language (a language for Java Virtual Machine) to parse scientific names. It is based on a Parsing Expression Grammar. The parser can be applied to scientific names of any complexity. It assigns a semantic meaning (such as genus name, species epithet, rank, year of publication, authorship, annotations, etc.) to all elements of a name. It is able to work with nested structures as in the names of hybrids. gnparser performs with ≈99% accuracy and processes 30 million name-strings/hour per CPU thread. The gnparser library is compatible with Scala, Java, R, Jython, and JRuby. The parser can be used as a command line application, as a socket server, a web-app or as a RESTful HTTP-service. It is released under an Open source MIT license.
CONCLUSIONS: Global Names Parser (gnparser) is a fast, high precision tool for biodiversity informaticians and biologists working with large numbers of scientific names. It can replace expensive and error-prone manual parsing and standardization of scientific names in many situations, and can quickly enhance the interoperability of distributed biological information.

PMID: 28549446 [PubMed - in process]

Semantic Technologies and Bio-Ontologies.

Methods Mol Biol. 2017;1617:83-91

Authors: Gutierrez F

Abstract
As information available through data repositories constantly grows, the need for automated mechanisms for linking, querying, and sharing data has become a relevant factor both in research and industry. This situation is more evident in research fields such as the life sciences, where new experiments by different research groups are constantly generating new information regarding a wide variety of related study objects. However, current methods for representing information and knowledge are not suited for machine processing. The Semantic Technologies are a set of standards and protocols that intend to provide methods for representing and handling data that encourages reusability of information and is machine-readable. In this chapter, we will provide a brief introduction to Semantic Technologies, and how these protocols and standards have been incorporated into the life sciences to facilitate dissemination and access to information.

PMID: 28540678 [PubMed - in process]

ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System.

PLoS One. 2016;11(10):e0164569

Authors: Storck M, Krumm R, Dugas M

Abstract
INTRODUCTION: Medical documentation is applied in various settings including patient care and clinical research. Since procedures of medical documentation are heterogeneous and developed further, secondary use of medical data is complicated. Development of medical forms, merging of data from different sources and meta-analyses of different data sets are currently a predominantly manual process and therefore difficult and cumbersome. Available applications to automate these processes are limited. In particular, tools to compare multiple documentation forms are missing. The objective of this work is to design, implement and evaluate the new system ODMSummary for comparison of multiple forms with a high number of semantically annotated data elements and a high level of usability.
METHODS: System requirements are the capability to summarize and compare a set of forms, enable to estimate the documentation effort, track changes in different versions of forms and find comparable items in different forms. Forms are provided in Operational Data Model format with semantic annotations from the Unified Medical Language System. 12 medical experts were invited to participate in a 3-phase evaluation of the tool regarding usability.
RESULTS: ODMSummary (available at https://odmtoolbox.uni-muenster.de/summary/summary.html) provides a structured overview of multiple forms and their documentation fields. This comparison enables medical experts to assess multiple forms or whole datasets for secondary use. System usability was optimized based on expert feedback.
DISCUSSION: The evaluation demonstrates that feedback from domain experts is needed to identify usability issues. In conclusion, this work shows that automatic comparison of multiple forms is feasible and the results are usable for medical experts.

PMID: 27736972 [PubMed - indexed for MEDLINE]

A Web Based Tool to Enhance Monitoring and Retention in Care for Tuberculosis Affected Patients.

Stud Health Technol Inform. 2017;237:204-208

Authors: Giannini B, Riccardi N, Di Biagio A, Cenderello G, Giacomini M

Abstract
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. Data can be manually input through the web interface or can be automatically imported from hospitals Laboratory Information Systems without human intervention. Automatic data import enhances data reuse and prevents errors introduction and time wasting. The network is an implementation of the Healthcare Services Specification Project (HSSP), as the Retrieve, Locate, and Update Service (RLUS) is used to manage patients' data. Clinical data are shared through standard HL7 Clinical Document Architecture (CDA) documents. Semantic interoperability is granted by the adoption of LOINC and ATC codes.

PMID: 28479569 [PubMed - in process]

Neuro-symbolic representation learning on biological knowledge graphs.

Bioinformatics. 2017 Apr 25;:

Authors: Alshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, Hoehndorf R

Abstract
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. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of SemanticWeb based knowledge bases in biology to use in machine learning and data analytics.
Availability and Implementation: https://github.com/bio-ontology-research-group/walking-rdf-and-owl.
Contact: robert.hoehndorf@kaust.edu.sa.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28449114 [PubMed - as supplied by publisher]

A Digital Framework to Support Providers and Patients in Diabetes Related Behavior Modification.

Stud Health Technol Inform. 2017;235:589-593

Authors: Abidi S, Vallis M, Piccinini-Vallis H, Imran SA, Abidi SSR

Abstract
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. We present the qualitative analysis of D-WISE usability by both physicians and patients.

PMID: 28423861 [PubMed - in process]

An Approach for the Support of Semantic Workflows in Electronic Health Records.

Stud Health Technol Inform. 2017;235:501-505

Authors: Schweitzer M, Hoerbst A

Abstract
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. This work gives an overview of the planned architecture and its main components. The architecture constitutes the basis for the prototype implementation of all components. With its highly dynamic structure based on SWS, the architecture will be able to cope with both, the individual users' needs as well as the quick evolving healthcare domain.

PMID: 28423843 [PubMed - in process]

Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics.

Stud Health Technol Inform. 2017;235:486-490

Authors: Rajabi E, Abidi SSR

Abstract
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.

PMID: 28423840 [PubMed - in process]