Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.

PLoS One. 2018;13(1):e0191263

Authors: Bibault JE, Zapletal E, Rance B, Giraud P, Burgun A

Abstract
PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant amount of patients with a high level of clinical details, multicenter studies are necessary. A challenge in creating high quality Big Data studies involving several treatment centers is the lack of semantic interoperability between data sources. We present the ontology we developed to address this issue.
METHODS: Radiation Oncology anatomical and target volumes were categorized in anatomical and treatment planning classes. International delineation guidelines specific to radiation oncology were used for lymph nodes areas and target volumes. Hierarchical classes were created to generate The Radiation Oncology Structures (ROS) Ontology. The ROS was then applied to the data from our institution.
RESULTS: Four hundred and seventeen classes were created with a maximum of 14 children classes (average = 5). The ontology was then converted into a Web Ontology Language (.owl) format and made available online on Bioportal and GitHub under an Apache 2.0 License. We extracted all structures delineated in our department since the opening in 2001. 20,758 structures were exported from our "record-and-verify" system, demonstrating a significant heterogeneity within a single center. All structures were matched to the ROS ontology before integration into our clinical data warehouse (CDW).
CONCLUSION: In this study we describe a new ontology, specific to radiation oncology, that reports all anatomical and treatment planning structures that can be delineated. This ontology will be used to integrate dosimetric data in the Assistance Publique-Hôpitaux de Paris CDW that stores data from 6.5 million patients (as of February 2017).

PMID: 29351341 [PubMed - in process]

OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system.

J Biomed Semantics. 2018 Jan 18;9(1):5

Authors: Senderov V, Simov K, Franz N, Stoev P, Catapano T, Agosti D, Sautter G, Morris RA, Penev L

Abstract
BACKGROUND: The biodiversity domain, and in particular biological taxonomy, is moving in the direction of semantization of its research outputs. The present work introduces OpenBiodiv-O, the ontology that serves as the basis of the OpenBiodiv Knowledge Management System. Our intent is to provide an ontology that fills the gaps between ontologies for biodiversity resources, such as DarwinCore-based ontologies, and semantic publishing ontologies, such as the SPAR Ontologies. We bridge this gap by providing an ontology focusing on biological taxonomy.
RESULTS: OpenBiodiv-O introduces classes, properties, and axioms in the domains of scholarly biodiversity publishing and biological taxonomy and aligns them with several important domain ontologies (FaBiO, DoCO, DwC, Darwin-SW, NOMEN, ENVO). By doing so, it bridges the ontological gap across scholarly biodiversity publishing and biological taxonomy and allows for the creation of a Linked Open Dataset (LOD) of biodiversity information (a biodiversity knowledge graph) and enables the creation of the OpenBiodiv Knowledge Management System. A key feature of the ontology is that it is an ontology of the scientific process of biological taxonomy and not of any particular state of knowledge. This feature allows it to express a multiplicity of scientific opinions. The resulting OpenBiodiv knowledge system may gain a high level of trust in the scientific community as it does not force a scientific opinion on its users (e.g. practicing taxonomists, library researchers, etc.), but rather provides the tools for experts to encode different views as science progresses.
CONCLUSIONS: OpenBiodiv-O provides a conceptual model of the structure of a biodiversity publication and the development of related taxonomic concepts. It also serves as the basis for the OpenBiodiv Knowledge Management System.

PMID: 29347997 [PubMed - in process]

MIRO: guidelines for minimum information for the reporting of an ontology.

J Biomed Semantics. 2018 Jan 18;9(1):6

Authors: Matentzoglu N, Malone J, Mungall C, Stevens R

Abstract
BACKGROUND: Creation and use of ontologies has become a mainstream activity in many disciplines, in particular, the biomedical domain. Ontology developers often disseminate information about these ontologies in peer-reviewed ontology description reports. There appears to be, however, a high degree of variability in the content of these reports. Often, important details are omitted such that it is difficult to gain a sufficient understanding of the ontology, its content and method of creation.
RESULTS: We propose the Minimum Information for Reporting an Ontology (MIRO) guidelines as a means to facilitate a higher degree of completeness and consistency between ontology documentation, including published papers, and ultimately a higher standard of report quality. A draft of the MIRO guidelines was circulated for public comment in the form of a questionnaire, and we subsequently collected 110 responses from ontology authors, developers, users and reviewers. We report on the feedback of this consultation, including comments on each guideline, and present our analysis on the relative importance of each MIRO information item. These results were used to update the MIRO guidelines, mainly by providing more detailed operational definitions of the individual items and assigning degrees of importance. Based on our revised version of MIRO, we conducted a review of 15 recently published ontology description reports from three important journals in the Semantic Web and Biomedical domain and analysed them for compliance with the MIRO guidelines. We found that only 41.38% of the information items were covered by the majority of the papers (and deemed important by the survey respondents) and a large number of important items are not covered at all, like those related to testing and versioning policies.
CONCLUSIONS: We believe that the community-reviewed MIRO guidelines can contribute to improving significantly the quality of ontology description reports and other documentation, in particular by increasing consistent reporting of important ontology features that are otherwise often neglected.

PMID: 29347969 [PubMed - in process]

BRIDG: a domain information model for translational and clinical protocol-driven research.

J Am Med Inform Assoc. 2017 Sep 01;24(5):882-890

Authors: Becnel LB, Hastak S, Ver Hoef W, Milius RP, Slack M, Wold D, Glickman ML, Brodsky B, Jaffe C, Kush R, Helton E

Abstract
Background: It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data.
Objective: To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG.
Methods: BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models.
Results: With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains.
Interpretation: The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

PMID: 28339791 [PubMed - indexed for MEDLINE]

Structural priming can inform syntactic analyses of partially grammaticalized constructions.

Behav Brain Sci. 2017 Jan;40:e288

Authors: Francis EJ

Abstract
Branigan & Pickering (B&P) argue successfully that structural priming provides valuable information for developing psychologically plausible syntactic and semantic theories. I discuss how their approach can be used to help determine whether partially grammaticalized constructions that have undergone semantic change also have undergone syntactic reanalysis. I then consider cases in which evidence from priming cannot distinguish between competing syntactic analyses.

PMID: 29342713 [PubMed - in process]

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability.

J Biomed Semantics. 2018 Jan 12;9(1):3

Authors: He Y, Xiang Z, Zheng J, Lin Y, Overton JA, Ong E

Abstract
Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an "eXtensible Ontology Development" (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).

PMID: 29329592 [PubMed - in process]

[Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service].

J Korean Acad Nurs. 2017 Dec;47(6):806-816

Authors: Kim M, Choi M, Youm Y

Abstract
PURPOSE: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.
METHODS: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.
RESULTS: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality.
CONCLUSION: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

PMID: 29326411 [PubMed - in process]

Biotea: semantics for Pubmed Central.

PeerJ. 2018;6:e4201

Authors: Garcia A, Lopez F, Garcia L, Giraldo O, Bucheli V, Dumontier M

Abstract
A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http://biotea.github.io.

PMID: 29312824 [PubMed]

Inferring ontology graph structures using OWL reasoning.

BMC Bioinformatics. 2018 Jan 05;19(1):7

Authors: Rodríguez-García MÁ, Hoehndorf R

Abstract
BACKGROUND: Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge.
RESULTS: We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .
CONCLUSIONS: Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

PMID: 29304741 [PubMed - in process]

KQA: A Knowledge Quality Assessment Model for Clinical Decision Support Systems.

Stud Health Technol Inform. 2017;245:983-986

Authors: Zolhavarieh S, Parry D

Abstract
Informatics researchers have developed many methods for using computers to utilize knowledge in decision making in the form of clinical decision support systems (CDSSs). These systems can enhance human decision making in the healthcare domain. The knowledge acquisition bottleneck is one of the well-known issues in developing knowledge-based systems such as CDSS. It can be considered as a flow of knowledge from different knowledge sources to the main system. Most existing methods for extracting knowledge from knowledge resources suffer from the lack of a proper mechanism for extracting high-quality knowledge. In this paper, we propose a framework to discover high-quality knowledge by utilizing Semantic Web technologies.

PMID: 29295247 [PubMed - in process]

Semantic Web Service Delivery in Healthcare Based on Functional and Non-Functional Properties.

Stud Health Technol Inform. 2017;245:945-949

Authors: Schweitzer M, Gorfer T, Hörbst A

Abstract
In the past decades, a lot of endeavor has been made on the trans-institutional exchange of healthcare data through electronic health records (EHR) in order to obtain a lifelong, shared accessible health record of a patient. Besides basic information exchange, there is a growing need for Information and Communication Technology (ICT) to support the use of the collected health data in an individual, case-specific workflow-based manner. This paper presents the results on how workflows can be used to process data from electronic health records, following a semantic web service approach that enables automatic discovery, composition and invocation of suitable web services. Based on this solution, the user (physician) can define its needs from a domain-specific perspective, whereas the ICT-system fulfills those needs with modular web services. By involving also non-functional properties for the service selection, this approach is even more suitable for the dynamic medical domain.

PMID: 29295239 [PubMed - in process]

Open IoT Ecosystem for Enhanced Interoperability in Smart Cities-Example of Métropole De Lyon.

Sensors (Basel). 2017 Dec 08;17(12):

Authors: Robert J, Kubler S, Kolbe N, Cerioni A, Gastaud E, Främling K

Abstract
The Internet of Things (IoT) has promised a future where everything gets connected. Unfortunately, building a single global ecosystem of Things that communicate with each other seamlessly is virtually impossible today. The reason is that the IoT is essentially a collection of isolated "Intranets of Things", also referred to as "vertical silos", which cannot easily and efficiently interact with each other. Smart cities are perhaps the most striking examples of this problem since they comprise a wide range of stakeholders and service providers who must work together, including urban planners, financial organisations, public and private service providers, telecommunication providers, industries, citizens, and so forth. Within this context, the contribution of this paper is threefold: (i) discuss business and technological implications as well as challenges of creating successful open innovation ecosystems, (ii) present the technological building blocks underlying an IoT ecosystem developed in the framework of the EU Horizon 2020 programme, (iii) present a smart city pilot (Heat Wave Mitigation in Métropole de Lyon) for which the proposed ecosystem significantly contributes to improving interoperability between a number of system components, and reducing regulatory barriers for joint service co-creation practices.

PMID: 29292719 [PubMed - in process]

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]