Outcomes of Ventilated Patients With Sepsis Who Undergo Interhospital Transfer: A Nationwide Linked Analysis.

Crit Care Med. 2018 Jan;46(1):e81-e86

Authors: Rush B, Tyler PD, Stone DJ, Geisler BP, Walley KR, Celi LA

Abstract
OBJECTIVES: The outcomes of critically ill patients who undergo interhospital transfer are not well understood. Physicians assume that patients who undergo interhospital transfer will receive more advanced care that may translate into decreased morbidity or mortality relative to a similar patient who is not transferred. However, there is little empirical evidence to support this assumption. We examined country-level U.S. data from the Nationwide Readmissions Database to examine whether, in mechanically ventilated patients with sepsis, interhospital transfer is associated with a mortality benefit.
DESIGN: Retrospective data analysis using complex survey design regression methods with propensity score matching.
SETTING: The Nationwide Readmissions Database contains information about hospital admissions from 22 States, accounting for roughly half of U.S. hospitalizations; the database contains linkage numbers so that admissions and transfers for the same patient can be linked across 1 year of follow-up.
PATIENTS: From the 2013 Nationwide Readmission Database Sample, 14,325,172 hospital admissions were analyzed. There were 61,493 patients with sepsis and on mechanical ventilation. Of these, 1,630 patients (2.7%) were transferred during their hospitalization. A propensity-matched cohort of 1,630 patients who did not undergo interhospital transfer was identified.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: The exposure of interest was interhospital transfer to an acute care facility. The primary outcome was hospital mortality; the secondary outcome was hospital length of stay. The propensity score included age, gender, insurance coverage, do not resuscitate status, use of renal replacement therapy, presence of shock, and Elixhauser comorbidities index. After propensity matching, interhospital transfer was not associated with a difference in in-hospital mortality (12.3% interhospital transfer vs 12.7% non-interhospital transfer; p = 0.74). However, interhospital transfer was associated with a longer total hospital length of stay (12.8 d interquartile range, 7.7-21.6 for interhospital transfer vs 9.1 d interquartile range, 5.1-17.0 for non-interhospital transfer; p < 0.01).
CONCLUSIONS: Patients with sepsis requiring mechanical ventilation who underwent interhospital transfer did not have improved outcomes compared with a cohort with matched characteristics who were not transferred. The study raises questions about the risk-benefit profile of interhospital transfer as an intervention.

PMID: 29068858 [PubMed - indexed for MEDLINE]

Development, dissemination, and applications of a new terminological resource, the Q-Code taxonomy for professional aspects of general practice/family medicine.

Eur J Gen Pract. 2017 Dec 15;:1-6

Authors: Jamoulle M, Resnick M, Grosjean J, Ittoo A, Cardillo E, Vander Stichele R, Darmoni S, Vanmeerbeek M

Abstract
BACKGROUND: While documentation of clinical aspects of General Practice/Family Medicine (GP/FM) is assured by the International Classification of Primary Care (ICPC), there is no taxonomy for the professional aspects (context and management) of GP/FM.
OBJECTIVES: To present the development, dissemination, applications, and resulting face validity of the Q-Codes taxonomy specifically designed to describe contextual features of GP/FM, proposed as an extension to the ICPC.
DEVELOPMENT: The Q-Codes taxonomy was developed from Lamberts' seminal idea for indexing contextual content (1987) by a multi-disciplinary team of knowledge engineers, linguists and general practitioners, through a qualitative and iterative analysis of 1702 abstracts from six GP/FM conferences using Atlas.ti software. A total of 182 concepts, called Q-Codes, representing professional aspects of GP/FM were identified and organized in a taxonomy. Dissemination: The taxonomy is published as an online terminological resource, using semantic web techniques and web ontology language (OWL) ( http://www.hetop.eu/Q ). Each Q-Code is identified with a unique resource identifier (URI), and provided with preferred terms, and scope notes in ten languages (Portuguese, Spanish, English, French, Dutch, Korean, Vietnamese, Turkish, Georgian, German) and search filters for MEDLINE and web searches.
APPLICATIONS: This taxonomy has already been used to support queries in bibliographic databases (e.g., MEDLINE), to facilitate indexing of grey literature in GP/FM as congress abstracts, master theses, websites and as an educational tool in vocational teaching, Conclusions: The rapidly growing list of practical applications provides face-validity for the usefulness of this freely available new terminological resource.

PMID: 29243572 [PubMed - as supplied by publisher]

The meaning of menarche: A cross-cultural semantic network analysis.

Health Care Women Int. 2017 Sep;38(9):971-982

Authors: Marván ML, Chrisler JC, Gorman JA, Barney A

Abstract
The psychological meaning of menarche was explored in 102 college students from Mexico and the United States. The Natural Semantic Networks Technique was used and participants were asked to respond to the prompt "My first period was …" The strongest components of the Mexican women's semantic network were scary, confusing, and unexpected; the strongest components of the American women's semantic network were unexpected, annoying, and painful. Only the Americans listed positive words (i.e., nice). The Mexicans' network contained the most negative words (i.e., dirty). The results suggest a need for better education and greater social support, especially in Mexico.

PMID: 28586269 [PubMed - indexed for MEDLINE]

A semantic-based workflow for biomedical literature annotation.

Database (Oxford). 2017 Jan 01;2017:

Authors: Sernadela P, Oliveira JL

Abstract
Computational annotation of textual information has taken on an important role in knowledge extraction from the biomedical literature, since most of the relevant information from scientific findings is still maintained in text format. In this endeavour, annotation tools can assist in the identification of biomedical concepts and their relationships, providing faster reading and curation processes, with reduced costs. However, the separate usage of distinct annotation systems results in highly heterogeneous data, as it is difficult to efficiently combine and exchange this valuable asset. Moreover, despite the existence of several annotation formats, there is no unified way to integrate miscellaneous annotation outcomes into a reusable, sharable and searchable structure. Taking up this challenge, we present a modular architecture for textual information integration using semantic web features and services. The solution described allows the migration of curation data into a common model, providing a suitable transition process in which multiple annotation data can be integrated and enriched, with the possibility of being shared, compared and reused across semantic knowledge bases.

PMID: 29220478 [PubMed - in process]

OWL-NETS: Transforming OWL Representations for Improved Network Inference.

Pac Symp Biocomput. 2018;23:133-144

Authors: Callahan TJ, Baumgartner WA, Bada M, Stefanski AL, Tripodi I, White EK, Hunter LE

Abstract
Our knowledge of the biological mechanisms underlying complex human disease is largely incomplete. While Semantic Web technologies, such as the Web Ontology Language (OWL), provide powerful techniques for representing existing knowledge, well-established OWL reasoners are unable to account for missing or uncertain knowledge. The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge. Therefore, robust methods which facilitate inductive inference on rich OWL-encoded knowledge are needed. Here, we propose OWL-NETS (NEtwork Transformation for Statistical learning), a novel computational method that reversibly abstracts OWL-encoded biomedical knowledge into a network representation tailored for network inference. Using several examples built with the Open Biomedical Ontologies, we show that OWL-NETS can leverage existing ontology-based knowledge representations and network inference methods to generate novel, biologically-relevant hypotheses. Further, the lossless transformation of OWL-NETS allows for seamless integration of inferred edges back into the original knowledge base, extending its coverage and completeness.

PMID: 29218876 [PubMed - in process]

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer.

Biomed Res Int. 2017;2017:8327980

Authors: Sernadela P, González-Castro L, Carta C, van der Horst E, Lopes P, Kaliyaperumal R, Thompson M, Thompson R, Queralt-Rosinach N, Lopez E, Wood L, Robertson A, Lamanna C, Gilling M, Orth M, Merino-Martinez R, Posada M, Taruscio D, Lochmüller H, Robinson P, Roos M, Oliveira JL

Abstract
Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.

PMID: 29214177 [PubMed - in process]

SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles.

Bioinformatics. 2017 Nov 23;:

Authors: Koehorst JJ, Dam JCJV, Saccenti E, Martins Dos Santos VAP, Suarez-Diez M, Schaap PJ

Abstract
Summary: To unlock the full potential of genome data and to enhance data interoperability and reusability of genome annotations we have developed SAPP, a Semantic Annotation Platform with Provenance. SAPP is designed as an infrastructure supporting FAIR de novo computational genomics but can also be used to process and analyse existing genome annotations. SAPP automatically predicts, tracks and stores structural and functional annotations and associated dataset- and element-wise provenance in a Linked Data format, thereby enabling information mining and retrieval with Semantic Web technologies. This greatly reduces the administrative burden of handling multiple analysis tools and versions thereof and facilitates multi-level large scale comparative analysis.
Availability: SAPP is written in JAVA and freely available at https://gitlab.com/sapp and runs on Unix-like operating systems. The documentation, examples and a tutorial are available at https://sapp.gitlab.io.
Contact: jasperkoehorst@gmail.com.

PMID: 29186322 [PubMed - as supplied by publisher]

A cloud-based framework for large-scale traditional Chinese medical record retrieval.

J Biomed Inform. 2017 Nov 21;:

Authors: Liu L, Liu L, Fu X, Huang Q, Zhang X, Zhang Y

Abstract
INTRODUCTION: Electronic medical records are increasingly common in medical practice. The secondary use of medical records has become increasingly important. It relies on the ability to retrieve the complete information about desired patient populations. How to effectively and accurately retrieve relevant medical records from large- scale medical big data is becoming a big challenge. Therefore, we propose an efficient and robust framework based on cloud for large-scale Traditional Chinese Medical Records (TCMRs) retrieval.
METHODS: We propose a parallel index building method and build a distributed search cluster, the former is used to improve the performance of index building, and the latter is used to provide high concurrent online TCMRs retrieval. Then, a real-time multi-indexing model is proposed to ensure the latest relevant TCMRs are indexed and retrieved in real-time, and a semantics-based query expansion method and a multi- factor ranking model are proposed to improve retrieval quality. Third, we implement a template-based visualization method for displaying medical reports.
RESULTS: The proposed parallel indexing method and distributed search cluster can improve the performance of index building and provide high concurrent online TCMRs retrieval. The multi-indexing model can ensure the latest relevant TCMRs are indexed and retrieved in real-time. The semantics expansion method and the multi-factor ranking model can enhance retrieval quality. The template-based visualization method can enhance the availability and universality, where the medical reports are displayed via friendly web interface.
CONCLUSIONS: In conclusion, compared with the current medical record retrieval systems, our system provides some advantages that are useful in improving the secondary use of large-scale traditional Chinese medical records in cloud environment. The proposed system is more easily integrated with existing clinical systems and be used in various scenarios.

PMID: 29175431 [PubMed - as supplied by publisher]

Using semantics for representing experimental protocols.

J Biomed Semantics. 2017 Nov 13;8(1):52

Authors: Giraldo O, García A, López F, Corcho O

Abstract
BACKGROUND: An experimental protocol is a sequence of tasks and operations executed to perform experimental research in biological and biomedical areas, e.g. biology, genetics, immunology, neurosciences, virology. Protocols often include references to equipment, reagents, descriptions of critical steps, troubleshooting and tips, as well as any other information that researchers deem important for facilitating the reusability of the protocol. Although experimental protocols are central to reproducibility, the descriptions are often cursory. There is the need for a unified framework with respect to the syntactic structure and the semantics for representing experimental protocols.
RESULTS: In this paper we present "SMART Protocols ontology", an ontology for representing experimental protocols. Our ontology represents the protocol as a workflow with domain specific knowledge embedded within a document. We also present the S ample I nstrument R eagent O bjective (SIRO) model, which represents the minimal common information shared across experimental protocols. SIRO was conceived in the same realm as the Patient Intervention Comparison Outcome (PICO) model that supports search, retrieval and classification purposes in evidence based medicine. We evaluate our approach against a set of competency questions modeled as SPARQL queries and processed against a set of published and unpublished protocols modeled with the SP Ontology and the SIRO model. Our approach makes it possible to answer queries such as Which protocols use tumor tissue as a sample.
CONCLUSION: Improving reporting structures for experimental protocols requires collective efforts from authors, peer reviewers, editors and funding bodies. The SP Ontology is a contribution towards this goal. We build upon previous experiences and bringing together the view of researchers managing protocols in their laboratory work. Website: https://smartprotocols.github.io/ .

PMID: 29132408 [PubMed - in process]

SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.

Artif Intell Med. 2017 Oct 27;:

Authors: Liu J, Zhao S, Wang G

Abstract
With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems.

PMID: 29111222 [PubMed - as supplied by publisher]

The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces.

Nucleic Acids Res. 2017 Nov 02;:

Authors: Altenhoff AM, Glover NM, Train CM, Kaleb K, Warwick Vesztrocy A, Dylus D, de Farias TM, Zile K, Stevenson C, Long J, Redestig H, Gonnet GH, Dessimoz C

Abstract
The Orthologous Matrix (OMA) is a leading resource to relate genes across many species from all of life. In this update paper, we review the recent algorithmic improvements in the OMA pipeline, describe increases in species coverage (particularly in plants and early-branching eukaryotes) and introduce several new features in the OMA web browser. Notable improvements include: (i) a scalable, interactive viewer for hierarchical orthologous groups; (ii) protein domain annotations and domain-based links between orthologous groups; (iii) functionality to retrieve phylogenetic marker genes for a subset of species of interest; (iv) a new synteny dot plot viewer; and (v) an overhaul of the programmatic access (REST API and semantic web), which will facilitate incorporation of OMA analyses in computational pipelines and integration with other bioinformatic resources. OMA can be freely accessed at https://omabrowser.org.

PMID: 29106550 [PubMed - as supplied by publisher]

Open chemistry: RESTful web APIs, JSON, NWChem and the modern web application.

J Cheminform. 2017 Oct 30;9(1):55

Authors: Hanwell MD, de Jong WA, Harris CJ

Abstract
An end-to-end platform for chemical science research has been developed that integrates data from computational and experimental approaches through a modern web-based interface. The platform offers an interactive visualization and analytics environment that functions well on mobile, laptop and desktop devices. It offers pragmatic solutions to ensure that large and complex data sets are more accessible. Existing desktop applications/frameworks were extended to integrate with high-performance computing resources, and offer command-line tools to automate interaction-connecting distributed teams to this software platform on their own terms. The platform was developed openly, and all source code hosted on the GitHub platform with automated deployment possible using Ansible coupled with standard Ubuntu-based machine images deployed to cloud machines. The platform is designed to enable teams to reap the benefits of the connected web-going beyond what conventional search and analytics platforms offer in this area. It also has the goal of offering federated instances, that can be customized to the sites/research performed. Data gets stored using JSON, extending upon previous approaches using XML, building structures that support computational chemistry calculations. These structures were developed to make it easy to process data across different languages, and send data to a JavaScript-based web client.

PMID: 29086154 [PubMed]

Electronic lab notebooks: can they replace paper?

J Cheminform. 2017 May 24;9(1):31

Authors: Kanza S, Willoughby C, Gibbins N, Whitby R, Frey JG, Erjavec J, Zupančič K, Hren M, Kovač K

Abstract
Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experiment. Countless electronic laboratory notebooks (ELNs) have been created in an attempt to digitise record keeping processes in the lab, but none of them have become a 'key player' in the ELN market, due to the many adoption barriers that have been identified in previous research and further explored in the user studies presented here. The main issues identified are the cost of the current available ELNs, their ease of use (or lack of it) and their accessibility issues across different devices and operating systems. Evidence suggests that whilst scientists willingly make use of generic notebooking software, spreadsheets and other general office and scientific tools to aid their work, current ELNs are lacking in the required functionality to meet the needs of the researchers. In this paper we present our extensive research and user study results to propose an ELN built upon a pre-existing cloud notebook platform that makes use of accessible popular scientific software and semantic web technologies to help overcome the identified barriers to adoption.

PMID: 29086051 [PubMed]

A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions.

J Med Syst. 2017 Oct 26;41(12):193

Authors: Abidi S

Abstract
Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework-termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS.

PMID: 29076113 [PubMed - in process]

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

J Healthc Eng. 2017;2017:4271273

Authors: Arch-Int N, Arch-Int S, Sonsilphong S, Wanchai P

Abstract
Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

PMID: 29065602 [PubMed - in process]

Best Paper Selection.

Yearb Med Inform. 2017 Aug;26(1):137-138

Authors:

Abstract
Lin FP, Pokorny A, Teng C, Dear R, Epstein RJ. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach. BMC Cancer 2016 Dec 1;16(1):929 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131452/ Marco-Ruiz L, Pedrinaci C, Maldonado JA, Panziera L, Chen R, Bellika JG. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. J Biomed Inform 2016 Aug;62:243-64 http://www.sciencedirect.com/science/article/pii/S153204641630065X?via%3Dihub McEvoy DS, Sittig DF, Hickman TT, Aaron S, Ai A, Amato M, Bauer DW, Fraser GM, Harper J, Kennemer A, Krall MA, Lehmann CU, Malhotra S, Murphy DR, O'Kelley B, Samal L, Schreiber R, Singh H, Thomas EJ, Vartian CV, Westmorland J, McCoy AB, Wright A. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017 Mar 1;24(2):331-8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391726/ Zamborlini V, Hoekstra R, Da Silveira M, Pruski C, ten Teije A, van Harmelen F. Inferring recommendation interactions in clinical guidelines. Semantic Web 2016;7(4):421-46 https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwin39e8r77VAhXEaFAKHeLkAC0QFgg_MAI&url=http%3A%2F%2Fwww.semantic-web-journal.net%2Fsystem%2Ffiles%2Fswj891.pdf&usg=AFQjCNF7CEXY49F929iQL09sRbGCl2Lc2A.

PMID: 29063554 [PubMed - in process]

Contributions from the 2016 Literature on Clinical Decision Support.

Yearb Med Inform. 2017 Aug;26(1):133-137

Authors: Koutkias V, Bouaud J

Abstract
Objectives: To summarize recent research and select the best papers published in 2016 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and section editor evaluation. Results: Among the 1,145 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper describes machine learning models used to predict breast cancer multidisciplinary team decisions and compares them with two predictors based on guideline knowledge. The second paper introduces a linked-data approach for publication, discovery, and interoperability of CDSSs. The third paper assessed the variation in high-priority drug-drug interaction (DDI) alerts across 14 Electronic Health Record systems, operating in different institutions in the US. The fourth paper proposes a generic framework for modeling multiple concurrent guidelines and detecting their recommendation interactions using semantic web technologies. Conclusions: The process of identifying and selecting best papers in the domain of CDSSs demonstrated that the research in this field is very active concerning diverse dimensions, such as the types of CDSSs, e.g. guideline-based, machine-learning-based, knowledge-fusion-based, etc., and addresses challenging areas, such as the concurrent application of multiple guidelines for comorbid patients, the resolution of interoperability issues, and the evaluation of CDSSs. Nevertheless, this process also showed that CDSSs are not yet fully part of the digitalized healthcare ecosystem. Many challenges remain to be faced with regard to the evidence of their output, the dissemination of their technologies, as well as their adoption for better and safer healthcare delivery.

PMID: 29063553 [PubMed - in process]

A two-staged approach to developing and evaluating an ontology for delivering personalized education to diabetic patients.

Inform Health Soc Care. 2017 Oct 16;:1-16

Authors: Quinn S, Bond R, Nugent C

Abstract
Ontologies are often used in biomedical and health domains to provide a concise and consistent means of attributing meaning to medical terminology. While they are novices in terms of ontology engineering, the evaluation of an ontology by domain specialists provides an opportunity to enhance its objectivity, accuracy, and coverage of the domain itself. This paper provides an evaluation of the viability of using ontology engineering novices to evaluate and enrich an ontology that can be used for personalized diabetic patient education. We describe a methodology for engaging healthcare and information technology specialists with a range of ontology engineering tasks. We used 87.8% of the data collected to validate the accuracy of our ontological model. The contributions also enabled a 16% increase in the class size and an 18% increase in object properties. Furthermore, we propose that ontology engineering novices can make valuable contributions to ontology development. Application-specific evaluation of the ontology using a semantic-web-based architecture is also discussed.

PMID: 29035605 [PubMed - as supplied by publisher]

Opinion Mining for Educational Video Lectures.

Adv Exp Med Biol. 2017;989:235-243

Authors: Kravvaris D, Kermanidis KL

Abstract
The search for relevant educational videos is a time consuming process for the users. Furthermore, the increasing demand for educational videos intensifies the problem and calls for the users to utilize whichever information is offered by the hosting web pages, and choose the most appropriate one. This research focuses on the classification of user views, based on the comments on educational videos, into positive or negative ones. The aim is to give users a picture of the positive and negative comments that have been recorded, so as to provide a qualitative view of the final selection at their disposal. The present paper's innovation is the automatic identification of the most important words of the verbal content of the video lectures and the filtering of the comments based on them, thus limiting the comments to the ones that have a substantial semantic connection with the video content.

PMID: 28971431 [PubMed - in process]

BioCarian: search engine for exploratory searches in heterogeneous biological databases.

BMC Bioinformatics. 2017 Oct 02;18(1):435

Authors: Zaki N, Tennakoon C

Abstract
BACKGROUND: There are a large number of biological databases publicly available for scientists in the web. Also, there are many private databases generated in the course of research projects. These databases are in a wide variety of formats. Web standards have evolved in the recent times and semantic web technologies are now available to interconnect diverse and heterogeneous sources of data. Therefore, integration and querying of biological databases can be facilitated by techniques used in semantic web. Heterogeneous databases can be converted into Resource Description Format (RDF) and queried using SPARQL language. Searching for exact queries in these databases is trivial. However, exploratory searches need customized solutions, especially when multiple databases are involved. This process is cumbersome and time consuming for those without a sufficient background in computer science. In this context, a search engine facilitating exploratory searches of databases would be of great help to the scientific community.
RESULTS: We present BioCarian, an efficient and user-friendly search engine for performing exploratory searches on biological databases. The search engine is an interface for SPARQL queries over RDF databases. We note that many of the databases can be converted to tabular form. We first convert the tabular databases to RDF. The search engine provides a graphical interface based on facets to explore the converted databases. The facet interface is more advanced than conventional facets. It allows complex queries to be constructed, and have additional features like ranking of facet values based on several criteria, visually indicating the relevance of a facet value and presenting the most important facet values when a large number of choices are available. For the advanced users, SPARQL queries can be run directly on the databases. Using this feature, users will be able to incorporate federated searches of SPARQL endpoints. We used the search engine to do an exploratory search on previously published viral integration data and were able to deduce the main conclusions of the original publication. BioCarian is accessible via http://www.biocarian.com .
CONCLUSIONS: We have developed a search engine to explore RDF databases that can be used by both novice and advanced users.

PMID: 28969593 [PubMed - in process]