Designing a CTSA-Based Social Network Intervention to Foster Cross-Disciplinary Team Science.

Clin Transl Sci. 2015 Aug;8(4):281-9

Authors: Vacca R, McCarty C, Conlon M, Nelson DR

Abstract
This paper explores the application of network intervention strategies to the problem of assembling cross-disciplinary scientific teams in academic institutions. In a project supported by the University of Florida (UF) Clinical and Translational Science Institute, we used VIVO, a semantic-web research networking system, to extract the social network of scientific collaborations on publications and awarded grants across all UF colleges and departments. Drawing on the notion of network interventions, we designed an alteration program to add specific edges to the collaboration network, that is, to create specific collaborations between previously unconnected investigators. The missing collaborative links were identified by a number of network criteria to enhance desirable structural properties of individual positions or the network as a whole. We subsequently implemented an online survey (N = 103) that introduced the potential collaborators to each other through their VIVO profiles, and investigated their attitudes toward starting a project together. We discuss the design of the intervention program, the network criteria adopted, and preliminary survey results. The results provide insight into the feasibility of intervention programs on scientific collaboration networks, as well as suggestions on the implementation of such programs to assemble cross-disciplinary scientific teams in CTSA institutions.

PMID: 25788258 [PubMed - indexed for MEDLINE]

Extraction of a group-pair relation: problem-solving relation from web-board documents.

Springerplus. 2016;5(1):1265

Authors: Pechsiri C, Piriyakul R

Abstract
This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical representation benefits non-professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem-concept EDU boundary and a solving-concept EDU boundary as two event-explanation groups, and how to determine the Problem-Solving relation between these two event-explanation groups. Therefore, we apply word co-occurrence to identify a problem-concept EDU and a solving-concept EDU, and machine-learning techniques to solve a problem-concept EDU boundary and a solving-concept EDU boundary. We propose using k-mean and Naïve Bayes to determine the Problem-Solving relation between the two event-explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group-pair relation extraction with high accuracy.

PMID: 27540498 [PubMed]

Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies.

Biomed Res Int. 2015;2015:976272

Authors: Sinaci AA, Laleci Erturkmen GB, Gonul S, Yuksel M, Invernizzi P, Thakrar B, Pacaci A, Cinar HA, Cicekli NK

Abstract
Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

PMID: 26543873 [PubMed - indexed for MEDLINE]

From Cues to Nudge: A Knowledge-Based Framework for Surveillance of Healthcare-Associated Infections.

J Med Syst. 2016 Jan;40(1):23

Authors: Shaban-Nejad A, Mamiya H, Riazanov A, Forster AJ, Baker CJ, Tamblyn R, Buckeridge DL

Abstract
We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guidelines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections - Knowledge in Use) framework enables hospitals to consistently follow the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relationships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection. By employing statistical inferencing in our study we use a set of heuristics to define the rule axioms to improve the SSI case detection. We also demonstrate how the occurrence of an SSI is identified using semantic e-triggers. The e-triggers will be used to improve our risk assessment of post-operative surgical site infections (SSIs) for patients undergoing certain type of surgeries (e.g., coronary artery bypass graft surgery (CABG)).

PMID: 26537131 [PubMed - indexed for MEDLINE]

GITIRBio: A Semantic and Distributed Service Oriented-Architecture for Bioinformatics Pipeline.

J Integr Bioinform. 2015;12(1):255

Authors: Castillo LF, López-Gartner G, Isaza GA, Sánchez M, Arango J, Agudelo-Valencia D, Castaño S

Abstract
The need to process large quantities of data generated from genomic sequencing has resulted in a difficult task for life scientists who are not familiar with the use of command-line operations or developments in high performance computing and parallelization. This knowledge gap, along with unfamiliarity with necessary processes, can hinder the execution of data processing tasks. Furthermore, many of the commonly used bioinformatics tools for the scientific community are presented as isolated, unrelated entities that do not provide an integrated, guided, and assisted interaction with the scheduling facilities of computational resources or distribution, processing and mapping with runtime analysis. This paper presents the first approximation of a Web Services platform-based architecture (GITIRBio) that acts as a distributed front-end system for autonomous and assisted processing of parallel bioinformatics pipelines that has been validated using multiple sequences. Additionally, this platform allows integration with semantic repositories of genes for search annotations. GITIRBio is available at: http://c-head.ucaldas.edu.co:8080/gitirbio.

PMID: 26527189 [PubMed - indexed for MEDLINE]

Social media and patient health outcomes. Findings from the yearbook 2014 section on consumer health informatics.

Yearb Med Inform. 2014;9:195-8

Authors: Staccini P, Douali N

Abstract
OBJECTIVES: To provide a review of the current excellent research published in the field of Consumer Health Informatics.
METHOD: We searched MEDLINE® and WEB OF SCIENCE® databases for papers published in 2013 in relation with Consumer Health Informatics. The authors identified 16 candidate best papers, which were then reviewed by four reviewers.
RESULTS: Five out of the 16 candidate papers were selected as best papers. One paper presents the key features of a system to automate the collection of web-based social media content for subsequent semantic annotation. This paper emphasizes the importance of mining social media to collect novel data from which new findings in drug abuse research were uncovered. The second paper presents a practical method to predict how a community structure would impact the spreading of information within the community. The third paper presents a method for improving the quality of online health communities. The fourth presents a new social network to allow the monitoring of the evolution of individuals' health status and diagnostic deficiencies, difficulties or barriers in rehabilitation. The last paper reports on teenage patients' perception on privacy and social media.
CONCLUSION: Selected papers not only show the value of using social media in the medical field but how to use these media to detect emergent diseases or risks, inform patients, promote disease prevention, and follow patients' opinion on healthcare resources.

PMID: 25123742 [PubMed - indexed for MEDLINE]

Managing free text for secondary use of health data.

Yearb Med Inform. 2014;9:167-9

Authors: Griffon N, Charlet J, Darmoni SJ

Abstract
OBJECTIVE: To summarize the best papers in the field of Knowledge Representation and Management (KRM).
METHODS: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013.
RESULTS: Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances.
CONCLUSION: NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

PMID: 25123738 [PubMed - indexed for MEDLINE]

The Evolution of Digital Chemistry at Southampton.

Mol Inform. 2015 Sep;34(9):585-597

Authors: Bird C, Coles SJ, Frey JG

Abstract
In this paper we take a historical view of e-Science and e-Research developments within the Chemical Sciences at the University of Southampton, showing the development of several stages of the evolving data ecosystem as Chemistry moves into the digital age of the 21(st) Century. We cover our research on aspects of the representation of chemical information in the context of the world wide web (WWW) and its semantic enhancement (the Semantic Web) and illustrate this with the example of the representation of quantities and units within the Semantic Web. We explore the changing nature of laboratories as computing power becomes increasing powerful and pervasive and specifically look at the function and role of electronic or digital notebooks. Having focussed on the creation of chemical data and information in context, we finish the paper by following the use and reuse of this data as facilitated by the features provided by digital repositories and their importance in facilitating the exchange of chemical information touching on the issues of open and or intelligent access to the data.

PMID: 27490710 [PubMed - as supplied by publisher]

Knowledge Acquisition during Exam Preparation Improves Memory and Modulates Memory Formation.

J Neurosci. 2016 Aug 3;36(31):8103-11

Authors: Brod G, Lindenberger U, Wagner AD, Shing YL

Abstract
UNLABELLED: According to the schema-relatedness hypothesis, new experiences that make contact with existing schematic knowledge are more easily encoded and remembered than new experiences that do not. Here we investigate how real-life gains in schematic knowledge affect the neural correlates of episodic encoding, assessing medical students 3 months before and immediately after their final exams. Human participants were scanned with functional magnetic resonance imaging while encoding associative information that varied in relatedness to medical knowledge (face-diagnosis vs face-name pairs). As predicted, improvements in memory performance over time were greater for face-diagnosis pairs (high knowledge-relevance) than for face-name pairs (low knowledge-relevance). Improved memory for face-diagnosis pairs was associated with smaller subsequent memory effects in the anterior hippocampus, along with increased functional connectivity between the anterior hippocampus and left middle temporal gyrus, a region important for the retrieval of stored conceptual knowledge. The decrease in the anterior hippocampus subsequent memory effect correlated with knowledge accumulation, as independently assessed by a web-based learning platform with which participants studied for their final exam. These findings suggest that knowledge accumulation sculpts the neural networks associated with successful memory formation, and highlight close links between knowledge acquired during studying and basic neurocognitive processes that establish durable memories.
SIGNIFICANCE STATEMENT: In a sample of medical students, we tracked knowledge accumulation via a web-based learning platform and investigated its effects on memory formation before and after participants' final medical exam. Knowledge accumulation led to significant gains in memory for knowledge-related events and predicted a selective decrease in hippocampal activation for successful memory formation. Furthermore, enhanced functional connectivity was found between hippocampus and semantic processing regions. These findings (1) demonstrate that knowledge facilitates binding in the hippocampus by enhancing its communication with the association cortices, (2) highlight close links between knowledge induced in the real world and basic neurocognitive processes that establish durable memories, and (3) exemplify the utility of combining laboratory-based cognitive neuroscience research with real-world educational technology for the study of memory.

PMID: 27488631 [PubMed - in process]

Semantic-Web Architecture for Electronic Discharge Summary Based on OWL 2.0 Standard.

Acta Inform Med. 2016 Jun;24(3):182-5

Authors: Tahmasebian S, Langarizadeh M, Ghazisaeidi M, Safdari R

Abstract
INTRODUCTION: Patients' electronic medical record contains all information related to treatment processes during hospitalization. One of the most important documents in this record is the record summary. In this document, summary of the whole treatment process is presented which is used for subsequent treatments and other issues pertaining to the treatment. Using suitable architecture for this document, apart from the aforementioned points we can use it in other fields such as data mining or decision making based on the cases.
MATERIAL AND METHODS: In this study, at first, a model for patient's medical record summary has been suggested using semantic web-based architecture. Then, based on service-oriented architecture and using Java programming language, a software solution was designed and run in a way to generate medical record summary with this structure and at the end, new uses of this structure was explained.
RESULTS: in this study a structure for medical record summaries along with corrective points within semantic web has been offered and a software running within Java along with special ontologies are provided.
DISCUSSION AND CONCLUSION: After discussing the project with the experts of medical/health data management and medical informatics as well as clinical experts, it became clear that suggested design for medical record summary apart from covering many issues currently faced in the medical records has also many advantages including its uses in research projects, decision making based on the cases etc.

PMID: 27482132 [PubMed]

Context-Awareness Based Personalized Recommendation of Anti-Hypertension Drugs.

J Med Syst. 2016 Sep;40(9):202

Authors: Chen D, Jin D, Goh TT, Li N, Wei L

Abstract
The World Health Organization estimates that almost one-third of the world's adult population are suffering from hypertension which has gradually become a "silent killer". Due to the varieties of anti-hypertensive drugs, patients are interested in how these drugs can be selected to match their respective conditions. This study provides a personalized recommendation service system of anti-hypertensive drugs based on context-awareness and designs a context ontology framework of the service. In addition, this paper introduces a Semantic Web Rule Language (SWRL)-based rule to provide high-level context reasoning and information recommendation and to overcome the limitation of ontology reasoning. To make the information recommendation of the drugs more personalized, this study also devises three categories of information recommendation rules that match different priority levels and uses a ranking algorithm to optimize the recommendation. The experiment conducted shows that combining the anti-hypertensive drugs personalized recommendation service context ontology (HyRCO) with the optimized rule reasoning can achieve a higher-quality personalized drug recommendation service. Accordingly this exploratory study of the personalized recommendation service for hypertensive drugs and its method can be easily adopted for other diseases.

PMID: 27473866 [PubMed - as supplied by publisher]

PepeSearch: Semantic Data for the Masses.

PLoS One. 2016;11(3):e0151573

Authors: Vega-Gorgojo G, Giese M, Heggestøyl S, Soylu A, Waaler A

Abstract
With the emergence of the Web of Data, there is a need of tools for searching and exploring the growing amount of semantic data. Unfortunately, such tools are scarce and typically require knowledge of SPARQL/RDF. We propose here PepeSearch, a portable tool for searching semantic datasets devised for mainstream users. PepeSearch offers a multi-class search form automatically constructed from a SPARQL endpoint. We have tested PepeSearch with 15 participants searching a Linked Open Data version of the Norwegian Register of Business Enterprises for non-trivial challenges. Retrieval performance was encouragingly high and usability ratings were also very positive, thus suggesting that PepeSearch is effective for searching semantic datasets by mainstream users. We also assessed its portability by configuring PepeSearch to query other SPARQL endpoints.

PMID: 26967899 [PubMed - indexed for MEDLINE]

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.

PLoS One. 2016;11(1):e0146576

Authors: Ranco G, Bordino I, Bormetti G, Caldarelli G, Lillo F, Treccani M

Abstract
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

PMID: 26808833 [PubMed - indexed for MEDLINE]

Web Image Search Re-ranking with Click-based Similarity and Typicality.

IEEE Trans Image Process. 2016 Jul 20;

Authors: Yang X, Mei T, Zhang YD, Liu J, Satoh S

Abstract
In image search re-ranking, besides the well known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the "implicit feedback" from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis visually similar images should be close in a ranking list and the strategy images with higher relevance should be ranked higher than others are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly. However, when measuring image similarity and typicality, conventional re-ranking approaches only consider visual information and initial ranks of images, while overlooking the influence of click-through data. This paper presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality (SCCST). First, to learn an appropriate similarity measurement, we propose click-based multi-feature similarity learning algorithm (CMSL), which conducts metric learning based on clickbased triplets selection, and integrates multiple features into a unified similarity space via multiple kernel learning. Then based on the learnt click-based image similarity measure, we conduct spectral clustering to group visually and semantically similar images into same clusters, and get the final re-rank list by calculating click-based clusters typicality and withinclusters click-based image typicality in descending order. Our experiments conducted on two real-world query-image datasets with diverse representative queries show that our proposed reranking approach can significantly improve initial search results, and outperform several existing re-ranking approaches.

PMID: 27448362 [PubMed - as supplied by publisher]

Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base.

Front Plant Sci. 2016;7:641

Authors: Rodríguez-Iglesias A, Rodríguez-González A, Irvine AG, Sesma A, Urban M, Hammond-Kosack KE, Wilkinson MD

Abstract
Pathogen-Host interaction data is core to our understanding of disease processes and their molecular/genetic bases. Facile access to such core data is particularly important for the plant sciences, where individual genetic and phenotypic observations have the added complexity of being dispersed over a wide diversity of plant species vs. the relatively fewer host species of interest to biomedical researchers. Recently, an international initiative interested in scholarly data publishing proposed that all scientific data should be "FAIR"-Findable, Accessible, Interoperable, and Reusable. In this work, we describe the process of migrating a database of notable relevance to the plant sciences-the Pathogen-Host Interaction Database (PHI-base)-to a form that conforms to each of the FAIR Principles. We discuss the technical and architectural decisions, and the migration pathway, including observations of the difficulty and/or fidelity of each step. We examine how multiple FAIR principles can be addressed simultaneously through careful design decisions, including making data FAIR for both humans and machines with minimal duplication of effort. We note how FAIR data publishing involves more than data reformatting, requiring features beyond those exhibited by most life science Semantic Web or Linked Data resources. We explore the value-added by completing this FAIR data transformation, and then test the result through integrative questions that could not easily be asked over traditional Web-based data resources. Finally, we demonstrate the utility of providing explicit and reliable access to provenance information, which we argue enhances citation rates by encouraging and facilitating transparent scholarly reuse of these valuable data holdings.

PMID: 27433158 [PubMed]

An ensemble method for extracting adverse drug events from social media.

Artif Intell Med. 2016 Jun;70:62-76

Authors: Liu J, Zhao S, Zhang X

Abstract
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media.
METHODS AND MATERIALS: We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter).
RESULTS: When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines.
CONCLUSIONS: Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness.

PMID: 27431037 [PubMed - in process]

FANTOM5 transcriptome catalog of cellular states based on Semantic MediaWiki.

Database (Oxford). 2016;2016

Authors: Abugessaisa I, Shimoji H, Sahin S, Kondo A, Harshbarger J, Lizio M, Hayashizaki Y, Carninci P, FANTOM consortium, Forrest A, Kasukawa T, Kawaji H

Abstract
The Functional Annotation of the Mammalian Genome project (FANTOM5) mapped transcription start sites (TSSs) and measured their activities in a diverse range of biological samples. The FANTOM5 project generated a large data set; including detailed information about the profiled samples, the uncovered TSSs at high base-pair resolution on the genome, their transcriptional initiation activities, and further information of transcriptional regulation. Data sets to explore transcriptome in individual cellular states encoded in the mammalian genomes have been enriched by a series of additional analysis, based on the raw experimental data, along with the progress of the research activities. To make the heterogeneous data set accessible and useful for investigators, we developed a web-based database called Semantic catalog of Samples, Transcription initiation And Regulators (SSTAR). SSTAR utilizes the open source wiki software MediaWiki along with the Semantic MediaWiki (SMW) extension, which provides flexibility to model, store, and display a series of data sets produced during the course of the FANTOM5 project. Our use of SMW demonstrates the utility of the framework for dissemination of large-scale analysis results. SSTAR is a case study in handling biological data generated from a large-scale research project in terms of maintenance and growth alongside research activities.Database URL: http://fantom.gsc.riken.jp/5/sstar/.

PMID: 27402679 [PubMed - in process]

Publication, Discovery and Interoperability of Clinical Decision Support Systems: a Linked Data Approach.

J Biomed Inform. 2016 Jul 8;

Authors: Marco-Ruiz L, Pedrinaci C, Maldonado JA, Panziera L, Chen R, Gustav Bellika J

Abstract
BACKGROUND: The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services' interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies.
OBJECTIVE: To facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data.
MATERIALS AND METHODS: We developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data.
RESULTS: We developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built.
DISCUSSION: Linked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine interpretable semantics to the CDS service description alleviating the challenges on interoperability and reuse. Linked Services allow for building 'digital libraries' of distributed CDS services that can be hosted and maintained in different organizations.

PMID: 27401856 [PubMed - as supplied by publisher]

Enhanced reproducibility of SADI web service workflows with Galaxy and Docker.

Gigascience. 2015;4:59

Authors: Aranguren ME, Wilkinson MD

Abstract
BACKGROUND: Semantic Web technologies have been widely applied in the life sciences, for example by data providers such as OpenLifeData and through web services frameworks such as SADI. The recently reported OpenLifeData2SADI project offers access to the vast OpenLifeData data store through SADI services.
FINDINGS: This article describes how to merge data retrieved from OpenLifeData2SADI with other SADI services using the Galaxy bioinformatics analysis platform, thus making this semantic data more amenable to complex analyses. This is demonstrated using a working example, which is made distributable and reproducible through a Docker image that includes SADI tools, along with the data and workflows that constitute the demonstration.
CONCLUSIONS: The combination of Galaxy and Docker offers a solution for faithfully reproducing and sharing complex data retrieval and analysis workflows based on the SADI Semantic web service design patterns.

PMID: 26640691 [PubMed - indexed for MEDLINE]

Developing a Data Element Repository to Support EHR-driven Phenotype Algorithm Authoring and Execution.

J Biomed Inform. 2016 Jul 5;

Authors: Jiang G, Kiefer RC, Rasmussen LV, Solbrig HR, Mo H, Pacheco JA, Xu J, Montague E, Thompson WK, Denny JC, Chute CG, Pathak J

Abstract
The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

PMID: 27392645 [PubMed - as supplied by publisher]