Thalia: Semantic search engine for biomedical abstracts.

Bioinformatics. 2018 Oct 17;:

Authors: Soto AJ, Przybyla P, Ananiadou S

Abstract
Summary: While publication rate of the biomedical literature has been growing steadily during the last decades, the accessibility of pertinent research publications for biologist and medical practitioners remains a challenge. This paper describes Thalia, which is a semantic search engine that can recognize eight different types of concepts occurring in biomedical abstracts. Thalia is available via a web-based interface or a RESTful API. A key aspect of our search engine is that it is updated from PubMed on a daily basis. We describe here the main building blocks of our tool as well as an evaluation of the retrieval capabilities of Thalia in the context of a precision medicine dataset.
Availability: Thalia is available at http://nactem.ac.uk/Thalia_BI/.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 30329013 [PubMed - as supplied by publisher]

Web-Based Information Infrastructure Increases the Interrater Reliability of Medical Coders: Quasi-Experimental Study.

J Med Internet Res. 2018 Oct 15;20(10):e274

Authors: Varghese J, Sandmann S, Dugas M

Abstract
BACKGROUND: Medical coding is essential for standardized communication and integration of clinical data. The Unified Medical Language System by the National Library of Medicine is the largest clinical terminology system for medical coders and Natural Language Processing tools. However, the abundance of ambiguous codes leads to low rates of uniform coding among different coders.
OBJECTIVE: The objective of our study was to measure uniform coding among different medical experts in terms of interrater reliability and analyze the effect on interrater reliability using an expert- and Web-based code suggestion system.
METHODS: We conducted a quasi-experimental study in which 6 medical experts coded 602 medical items from structured quality assurance forms or free-text eligibility criteria of 20 different clinical trials. The medical item content was selected on the basis of mortality-leading diseases according to World Health Organization data. The intervention comprised using a semiautomatic code suggestion tool that is linked to a European information infrastructure providing a large medical text corpus of >300,000 medical form items with expert-assigned semantic codes. Krippendorff alpha (Kalpha) with bootstrap analysis was used for the interrater reliability analysis, and coding times were measured before and after the intervention.
RESULTS: The intervention improved interrater reliability in structured quality assurance form items (from Kalpha=0.50, 95% CI 0.43-0.57 to Kalpha=0.62 95% CI 0.55-0.69) and free-text eligibility criteria (from Kalpha=0.19, 95% CI 0.14-0.24 to Kalpha=0.43, 95% CI 0.37-0.50) while preserving or slightly reducing the mean coding time per item for all 6 coders. Regardless of the intervention, precoordination and structured items were associated with significantly high interrater reliability, but the proportion of items that were precoordinated significantly increased after intervention (eligibility criteria: OR 4.92, 95% CI 2.78-8.72; quality assurance: OR 1.96, 95% CI 1.19-3.25).
CONCLUSIONS: The Web-based code suggestion mechanism improved interrater reliability toward moderate or even substantial intercoder agreement. Precoordination and the use of structured versus free-text data elements are key drivers of higher interrater reliability.

PMID: 30322834 [PubMed - in process]

Predictors of long-term care among nonagenarians: the Vitality 90 + Study with linked data of the care registers.

Aging Clin Exp Res. 2018 Aug;30(8):913-919

Authors: Kauppi M, Raitanen J, Stenholm S, Aaltonen M, Enroth L, Jylhä M

Abstract
BACKGROUND: The need for long-term care services increases with age. However, little is known about the predictors of long-term care (LTC) entry among the oldest old.
AIMS: Aim of this study was to assess predictors of LTC entry in a sample of men and women aged 90 years and older.
METHODS: This study was based on the Vitality 90 + Study, a population-based study of nonagenarians in the city of Tampere, Finland. Baseline information about health, functioning and living conditions were collected by mailed questionnaires. Information about LTC was drawn from care registers during the follow-up period extending up to 11 years. Cox regression models were used for the analyses, taking into account the competing risk of mortality.
RESULTS: During the mean follow-up period of 2.3 years, 844 (43%) subjects entered first time into LTC. Female gender (HR 1.39, 95% CI 1.14-1.69), having at least two chronic conditions (HR 1.24, 95% CI 1.07-1.44), living alone (HR 1.37, 95% CI 1.15-1.63) and help received sometimes (HR 1.23, 95% CI 1.02-1.49) or daily (HR 1.68, 95% CI 1.38-2.04) were independent predictors of LTC entry.
CONCLUSION: Risk of entering into LTC was increased among women, subjects with at least two chronic conditions, those living alone and with higher level of received help. Since number of nonagenarians will increase and the need of care thereby, it is essential to understand predictors of LTC entry to offer appropriate care for the oldest old in future.

PMID: 29222731 [PubMed - indexed for MEDLINE]

GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms.

Sci Rep. 2018 Oct 10;8(1):15107

Authors: Zhao C, Wang Z

Abstract
Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik's and Wang's methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node giving to its descendent nodes. GOGO can calculate functional similarities between genes and then cluster genes based on their functional similarities. Evaluations performed on multiple pathways retrieved from the saccharomyces genome database (SGD) show that GOGO can accurately and robustly cluster genes based on functional similarities. We release GOGO as a web server and also as a stand-alone tool, which allows convenient execution of the tool for a small number of GO terms or integration of the tool into bioinformatics pipelines for large-scale calculations. GOGO can be freely accessed or downloaded from http://dna.cs.miami.edu/GOGO/ .

PMID: 30305653 [PubMed - in process]

A survey of ontology learning techniques and applications.

Database (Oxford). 2018 Jan 01;2018:

Authors: Asim MN, Wasim M, Khan MUG, Mahmood W, Abbasi HM

Abstract
Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

PMID: 30295720 [PubMed - in process]

Using Semantic Web Technologies to Enable Cancer Genomics Discovery at Petabyte Scale.

Cancer Inform. 2018;17:1176935118774787

Authors: Cejovic J, Radenkovic J, Mladenovic V, Stanojevic A, Miletic M, Radanovic S, Bajcic D, Djordjevic D, Jelic F, Nesic M, Lau J, Grady P, Groves-Kirkby N, Kural D, Davis-Dusenbery B

Abstract
Increased efforts in cancer genomics research and bioinformatics are producing tremendous amounts of data. These data are diverse in origin, format, and content. As the amount of available sequencing data increase, technologies that make them discoverable and usable are critically needed. In response, we have developed a Semantic Web-based Data Browser, a tool allowing users to visually build and execute ontology-driven queries. This approach simplifies access to available data and improves the process of using them in analyses on the Seven Bridges Cancer Genomics Cloud (CGC; www.cancergenomicscloud.org). The Data Browser makes large data sets easily explorable and simplifies the retrieval of specific data of interest. Although initially implemented on top of The Cancer Genome Atlas (TCGA) data set, the Data Browser's architecture allows for seamless integration of other data sets. By deploying it on the CGC, we have enabled remote researchers to access data and perform collaborative investigations.

PMID: 30283230 [PubMed]

Cognitive Approaches for Medicine in Cloud Computing.

J Med Syst. 2018 Mar 03;42(4):70

Authors: Ogiela U, Takizawa M, Ogiela L

Abstract
This paper will present the application potential of the cognitive approach to data interpretation, with special reference to medical areas. The possibilities of using the meaning approach to data description and analysis will be proposed for data analysis tasks in Cloud Computing. The methods of cognitive data management in Cloud Computing are aimed to support the processes of protecting data against unauthorised takeover and they serve to enhance the data management processes. The accomplishment of the proposed tasks will be the definition of algorithms for the execution of meaning data interpretation processes in safe Cloud Computing.
HIGHLIGHTS: • We proposed a cognitive methods for data description. • Proposed a techniques for secure data in Cloud Computing. • Application of cognitive approaches for medicine was described.

PMID: 29502320 [PubMed - indexed for MEDLINE]

Supporting biomedical ontology evolution by identifying outdated concepts and the required type of change.

J Biomed Inform. 2018 Sep 08;:

Authors: Cardoso SD, Pruski C, Silveira MD

Abstract
The consistent evolution of ontologies is a major challenge for systems using semantically enriched data, for example, for annotating, indexing, or reasoning. The biomedical domain is a typical example where ontologies, expressed with different formalisms, have been used for a long time and whose dynamic nature requires the regular revision of underlying systems. However, the automatic identification of outdated concepts and proposition of revision actions to update them are still open research questions. Solutions to these problems are of great interest to organizations that manage huge and dynamic ontologies. In this paper, we present an approach for i) identifying the concepts of an ontology that require revision and ii) suggesting the type of revision. Our analysis is based on three aspects: structural information encoded in the ontology, relational information gained from external source of knowledge (i.e., PubMed and UMLS) and temporal information derived from the history of the ontology. Our approach aims to evaluate different methods and parameters used by supervised learning classifiers to identify both the set of concepts that need revision, and the type of revision. We applied our approach to four well-known biomedical ontologies / terminologies (ICD-9-CM, MeSH, NCIt and SNOMED CT) and compared our results to similar approaches. Our model shows accuracy ranging from 68% (for SNOMED CT) to 91% (for MeSH), and an average of 71% when considering all datasets together.

PMID: 30205172 [PubMed - as supplied by publisher]

Linked open data-based framework for automatic biomedical ontology generation.

BMC Bioinformatics. 2018 Sep 10;19(1):319

Authors: Alobaidi M, Malik KM, Sabra S

Abstract
BACKGROUND: Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns.
RESULTS: Our evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontology-learning framework called "OntoGain" which shows that LOD-ABOG performs 14.76% better in terms of relation extraction.
CONCLUSION: This paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.

PMID: 30200874 [PubMed - in process]

Representing vaccine misinformation using ontologies.

J Biomed Semantics. 2018 Aug 31;9(1):22

Authors: Amith M, Tao C

Abstract
BACKGROUND: In this paper, we discuss the design and development of a formal ontology to describe misinformation about vaccines. Vaccine misinformation is one of the drivers leading to vaccine hesitancy in patients. While there are various levels of vaccine hesitancy to combat and specific interventions to address those levels, it is important to have tools that help researchers understand this problem. With an ontology, not only can we collect and analyze varied misunderstandings about vaccines, but we can also develop tools that can provide informatics solutions.
RESULTS: We developed the Vaccine Misinformation Ontology (VAXMO) that extends the Misinformation Ontology and links to the nanopublication Resource Description Framework (RDF) model for false assertions of vaccines. Preliminary assessment using semiotic evaluation metrics indicated adequate quality for our ontology. We outlined and demonstrated proposed uses of the ontology to detect and understand anti-vaccine information.
CONCLUSION: We surmised that VAXMO and its proposed use cases can support tools and technology that can pave the way for vaccine misinformation detection and analysis. Using an ontology, we can formally structure knowledge for machines and software to better understand the vaccine misinformation domain.

PMID: 30170633 [PubMed - in process]

SNOMED CT standard ontology based on the ontology for general medical science.

BMC Med Inform Decis Mak. 2018 Aug 31;18(1):76

Authors: El-Sappagh S, Franda F, Ali F, Kwak KS

Abstract
BACKGROUND: Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT.
METHOD: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS).
RESULTS: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at http://bioportal.bioontology.org/ontologies/SCTO/ .
CONCLUSION: The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.

PMID: 30170591 [PubMed - in process]

A Method to Use Metadata in Legacy Web Applications: The Samply.MDR.Injector.

Stud Health Technol Inform. 2018;253:45-49

Authors: Kern J, Tas D, Ulrich H, Schmidt EE, Ingenerf J, Ückert F, Lablans M

Abstract
Whenever medical data is integrated from multiple sources, it is regarded good practice to separate data from information about its meaning, such as designations, definitions or permissible values (in short: metadata). However, the ways in which applications work with metadata are imperfect: Many applications do not support fetching metadata from externalized sources such as metadata repositories. In order to display human-readable metadata in any application, we propose not to change the application, but to provide a library that makes a change to the user interface. The goal of this work is to provide a way to "inject" the meaning of metadata keys into the web-based frontend of an application to make it "metadata aware".

PMID: 30147038 [PubMed - in process]

A Web Service to Suggest Semantic Codes Based on the MDM-Portal.

Stud Health Technol Inform. 2018;253:35-39

Authors: Hegselmann S, Storck M, Geßner S, Neuhaus P, Varghese J, Dugas M

Abstract
Annotation with semantic codes helps to overcome interoperability issues for electronic documentation in medicine. However, the manual annotation process is laborious and semantic codes are ambiguous. We developed a publicly accessible web service to alleviate these drawbacks with a sophisticated and fast search mechanism based on more than 330,000 semantic code suggestions. These suggestions are derived from semantically annotated data elements contained in the Portal of Medical Data Models manually curated by medical professionals. Integrating this suggestion service can support the manual annotation process and strengthen uniform coding. Integration is demonstrated for two separate data model editors. Usage statistics show over 5,500 suggestion requests per month for semantic annotation of items. The web service may also prove helpful for automatic semantic coding.

PMID: 30147036 [PubMed - in process]

The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques.

Med Phys. 2018 Aug 24;:

Authors: Traverso A, van Soest J, Wee L, Dekker A

Abstract
PURPOSE: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appealing, low-cost and noninvasive approach toward personalization. Machine learning algorithms could be trained over clinical "big data" to build prediction models for personalized therapy. To reach this goal, a scalable "big data" architecture for the medical domain becomes essential, based on data standardization to transform clinical data into FAIR (Findable, Accessible, Interoperable and Reusable) data. Using Ontologies and Semantic Web technologies, we attempt to reach mentioned goal.
METHODS: We developed an ontology to be used in the field of radiation oncology to map clinical data from relational databases. We combined ontology with semantic Web techniques to publish mapped data and easily query them using SPARQL.
RESULTS: The Radiation Oncology Ontology (ROO) contains 1,183 classes and 211 properties between classes to represent clinical data (and their relationships) in the radiation oncology domain following FAIR principles. We combined the ontology with Semantic Web technologies showing how to efficiently and easily integrate and query data from different (relational database) sources without a priori knowledge of their structures.
DISCUSSION: When clinical FAIR data sources are combined (linked data) using mentioned technologies, new relationships between entities are created and discovered, representing a dynamic body of knowledge that is continuously accessible and increasing.

PMID: 30144092 [PubMed - as supplied by publisher]

VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning.

IEEE Trans Vis Comput Graph. 2018 Aug 20;:

Authors: Sacha D, Kraus M, Keim DA, Chen M

Abstract
While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely "VA-assisted ML". The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.

PMID: 30130221 [PubMed - as supplied by publisher]

Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients.

Strahlenther Onkol. 2018 06;194(6):580-590

Authors: Peeken JC, Hesse J, Haller B, Kessel KA, Nüsslin F, Combs SE

Abstract
BACKGROUND: For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information.
METHODS: 189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan-Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined.
RESULTS: For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. "Multilocality," "deep white-matter invasion," "satellites," and "ependymal invasion" were over proportionally selected for multivariate model generation, underlining their importance.
CONCLUSIONS: We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

PMID: 29442128 [PubMed - indexed for MEDLINE]

EAPB: entropy-aware path-based metric for ontology quality.

J Biomed Semantics. 2018 Aug 10;9(1):20

Authors: Shen Y, Chen D, Tang B, Yang M, Lei K

Abstract
BACKGROUND: Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.
RESULTS: We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.
CONCLUSIONS: We leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate ).

PMID: 30097014 [PubMed - in process]

Common Consumer Health-Related Needs in the Pediatric Hospital Setting: Lessons from an Engagement Consultation Service.

Appl Clin Inform. 2018 Jul;9(3):595-603

Authors: Lee DJ, Cronin R, Robinson J, Anders S, Unertl K, Kelly K, Hankins H, Skeens R, Jackson GP

Abstract
BACKGROUND:  Informed and engaged parents may influence outcomes for childhood illness. Understanding the needs of the caregivers of pediatric patients is a critical first step in promoting engagement in their child's care. In 2014, we developed an Engagement Consultation Service at the Monroe Carell Jr. Children's Hospital at Vanderbilt. This service determines the health-related needs of the caregivers of hospitalized children and makes educational or technology recommendations to meet those needs and support engagement.
OBJECTIVES:  This report describes the most common health-related needs identified in the caregivers of hospitalized pediatric patients and details the recommended interventions to meet those needs.
METHODS:  The most commonly reported consumer health-related needs from our 3-year experience with the Engagement Consultation Service were extracted from consultations notes. Each need was classified by semantic type using a taxonomy of consumer health needs. Typical recommendations for each need and their administration were detailed.
RESULTS:  The most frequently recognized needs involved communicating with health care providers after discharge, using medical devices, distinguishing between benign and concerning symptoms, knowing what questions to ask providers and remembering them, finding trustworthy sources of information online, understanding disease prognosis, and getting emotional support. A variety of apps, Web sites, printed materials, and online groups were recommended.
CONCLUSION:  The parents of hospitalized patients share several common health-related needs that can be addressed with educational and technology interventions. An inpatient Engagement Consultation Service provides a generalizable framework for identifying health-related needs and delivers tools to meet those needs and promote engagement during and after hospitalizations.

PMID: 30089333 [PubMed - in process]

Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach.

J Integr Bioinform. 2018 Aug 07;:

Authors: Brandizi M, Singh A, Rawlings C, Hassani-Pak K

Abstract
The speed and accuracy of new scientific discoveries - be it by humans or artificial intelligence - depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles).

PMID: 30085931 [PubMed - as supplied by publisher]

An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival.

BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):41

Authors: Zhang H, Guo Y, Li Q, George TJ, Shenkman E, Modave F, Bian J

Abstract
BACKGROUND: Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges.
METHODS: Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs.
RESULTS: Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies.
CONCLUSIONS: Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.

PMID: 30066664 [PubMed - in process]