A Neural Network-Inspired Approach for Improved and True Movie Recommendations.

Comput Intell Neurosci. 2019;2019:4589060

Authors: Ibrahim M, Bajwa IS, Ul-Amin R, Kasi B

Abstract
In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

PMID: 31467517 [PubMed - in process]

Linking Health Records with Knowledge Sources Using OWL and RDF.

Stud Health Technol Inform. 2019;257:53-58

Authors: Chelsom J, Dogar N

Abstract
This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using schemes such as SNOMED, ICD or LOINC, with the coding applied statically, on the basis of the predefined structure, or dynamically, on the basis of data values entered in the health record. The highly structured, coded record can then be linked with external knowledge sources which are themselves coded using the Resource Description Framework. These methods have been used to implement dynamic decision support in the open source cityEHR health records system. The effectiveness of the decision support depends on the scope and quality of the clinical coding and the sophistication of the algorithm used to match the structured record with knowledge sources.

PMID: 30741172 [PubMed - indexed for MEDLINE]

The SNIK Graph: Visualization of a Medical Informatics Ontology.

Stud Health Technol Inform. 2019 Aug 21;264:1941-1942

Authors: Jahn F, Höffner K, Schneider B, Lörke A, Pause T, Ammenwerth E, Winter A

Abstract
SNIK, a medical informatics ontology, combines knowledge from different literature sources dealing with the management of hospital information systems (HIS). Concepts and relations were extracted from literature, modeled as an ontology and visualized as a graph on a website. We demonstrate the potential of the graph visualization for tuitional scenarios. SNIK complements teaching and learning with conventional literature by concentrating knowledge that is scattered over different pieces of text around one node of a graph.

PMID: 31438418 [PubMed - in process]

Open and Linkable Knowledge About Management of Health Information Systems.

Stud Health Technol Inform. 2019 Aug 21;264:1678-1679

Authors: Höffner K, Jahn F, Lörke A, Pause T, Schneider B, Ammenwerth E, Winter A

Abstract
Given a care delivery organization, its health information system can be defined as the part of the organization that processes and stores data, information, and knowledge. There is an enormous number of frameworks, textbooks and articles that describe the scope of health information system management from the perspective of medical informatics. Transforming this knowledge to Linked Open Data results in a structured data representation that is accessible for both humans and machines, the Semantic Network of Information Management in Hospitals (SNIK). We present interfaces that are useful for researchers, practitioners and students, depending on their objectives and their Semantic Web skills.

PMID: 31438289 [PubMed - in process]

TBench: A Collaborative Work Platform for Multilingual Terminology Editing and Development.

Stud Health Technol Inform. 2019 Aug 21;264:1449-1450

Authors: Deng P, Ji Y, Shen L, Li J, Ren H, Qian Q, Sun H

Abstract
Terminology facilitates consistent use and semantic integration of heterogeneous, multimodal data within and across domains. This paper presents TBench (Termilology Workbench) for multilingual terminology editing and development within a distributed environment. TBench is a web-service Java tool consisting of two main functionalities that are knowledge construction (i.e.extended model based on ISO25964, batch reusing and constructing multilingual concept hierarchy and relationships) and collaborative control in order to achieve custom extensions, reuse, multilingual alignment, integration and refactoring.

PMID: 31438175 [PubMed - in process]

Linked Open Data in the Biomedical Information Area: A Keywords Analysis.

Stud Health Technol Inform. 2019 Aug 21;264:1429-1430

Authors: Bonacina S

Abstract
The objective of this paper was to determine the extent of the usage of "Linked Open Data" within biomedical literature. Applying PRISMA statement for literature reviews, forty-six papers were included in the analysis and keywords identified. Keywords have been classified according to MeSH categories, when possible. Twenty-three keywords had a frequency > one, 146 keywords had a frequency equal to one. Two MeSH categories were recurring. Future work includes applying association rules learning to keywords.

PMID: 31438165 [PubMed - in process]

Using an Artificial Intelligence-Based Argument Theory to Generate Automated Patient Education Dialogues for Families of Children with Juvenile Idiopathic Arthritis.

Stud Health Technol Inform. 2019 Aug 21;264:1337-1341

Authors: Rose-Davis B, Van Woensel W, Stringer E, Abidi S, Abidi SSR

Abstract
Juvenile Idiopathic Arthritis (JIA) is the most common chronic rheumatic disease of childhood, with outcomes including pain, prolonged dependence on medications, and disability. Parents of children with JIA report being overwhelmed by the volume of information in the patient education materials that are available to them. This paper addresses this educational gap by applying an artificial intelligence method, based on an extended model of argument, to design and implement a dialogue system that allows users get the educational material they need, when they need it. In the developed system, the studied model of argument was leveraged as part of the system's dialogue manager. A qualitative evaluation of the system, using cognitive walkthroughs and semi-structured interviews with JIA domain experts, suggests that these methods show great promise for providing quality information to families of children with JIA when they need it.

PMID: 31438143 [PubMed - in process]

Implementation of Clinical Decision Support Services to Detect Potential Drug-Drug Interaction Using Clinical Quality Language.

Stud Health Technol Inform. 2019 Aug 21;264:724-728

Authors: Nguyen BP, Reese T, Decker S, Malone D, Boyce RD, Beyan O

Abstract
Potential drug-drug interactions (PDDI) rules are currently represented without any common standard making them difficult to update, maintain, and exchange. The PDDI minimum information model developed by the Semantic Web in the Healthcare and Life Sciences Community Group describes PDDI knowledge in an actionable format. In this paper, we report implementation and evaluation of CDS Services which represent PDDI knowledge with Clinical Quality Language (CQL). The suggested solution is based on emerging standards including CDS Hooks, FHIR, and CQL. Two use cases are selected, implemented with CQL rules and tested at the Connectathon held at the 32nd Annual Plenary & Working Group Meeting of HL7.

PMID: 31438019 [PubMed - in process]

Building an Experimental German User Interface Terminology Linked to SNOMED CT.

Stud Health Technol Inform. 2019 Aug 21;264:153-157

Authors: Hashemian Nik D, Kasáč Z, Goda Z, Semlitsch A, Schulz S

Abstract
We describe the process of creating a User Interface Terminology (UIT) with the goal to generate a maximum of German language interface terms that are mapped to the reference terminology SNOMED CT. The purpose is to offer a high coverage of medical jargon in order to optimise semantic annotations of clinical documents by text mining systems. The first step consisted in the creation of an n-gram table to which words and short phrases from the English SNOMED CT description table were automatically extracted and entered. The second step was to fill up the n-gram table with human and machine translations, manually enriched by POS tags. Top-down and bottom-up methods for manual terminology population were used. Grammar rules were formulated and embedded into a term generator, which then created one-to-many German variants per SNOMED CT description. Currently, the German user interface terminology contains 4,425,948 entries, created out of 111,605 German n-grams, assigned to 95,298 English n-grams. With 341,105 active concepts and 542,462 (non FSN) descriptions, it corresponds to an average of 13 interface terms per concept and 8.2 per description. An analysis of the current quality of this resource by blinded human assessment terminology states equivalence regarding term understandability compared to a fully automated Web-based translator, which, however does not yield any synonyms, so that there are good reasons to further develop this semi-automated terminology engineering method and recommend it for other language pairs.

PMID: 31437904 [PubMed - in process]

Compatible Data Models at Design Stage of Medical Information Systems: Leveraging Related Data Elements from the MDM Portal.

Stud Health Technol Inform. 2019 Aug 21;264:113-117

Authors: Dugas M, Hegselmann S, Riepenhausen S, Neuhaus P, Greulich L, Meidt A, Varghese J

Abstract
Compatible data models are key for data integration. Data transformation after data collection has many limitations. Therefore compatible data structures should be addressed already during the design of information systems. The portal of Medical Data Models (MDM), which contains 20.000+ models and 495.000+ data items, was enhanced with a web service to identify data elements, which are frequently collected together in real information systems. Using Apache Solr, a fast search functionality to identify those elements with semantic annotations was implemented. This service was integrated into the metadata registry (MDR) component of MDM to make it available to the scientific community. It can be used to build intelligent data model editors, which suggest and import frequent data element definitions according to the current medical context.

PMID: 31437896 [PubMed - in process]

Romedi: An Open Data Source About French Drugs on the Semantic Web.

Stud Health Technol Inform. 2019 Aug 21;264:79-82

Authors: Cossin S, Lebrun L, Lobre G, Loustau R, Jouhet V, Griffier R, Mougin F, Diallo G, Thiessard F

Abstract
The W3C project, "Linking Open Drug Data" (LODD), linked several publicly available sources of drug data together. So far, French data, like marketed drugs and their summary of product characteristics, were not integrated and remained difficult to query. In this paper, we present Romedi (Référentiel Ouvert du Médicament), an open dataset that links French data on drugs to international resources. The principles and standard recommendations created by the W3C for sharing information were adopted. Romedi was connected to the Unified Medical Language System and DrugBank, two central resources of the LODD project. A SPARQL endpoint is available to query Romedi and services are provided to annotate textual content with Romedi terms. This paper describes its content, its services, its links to external resources, and expected future developments.

PMID: 31437889 [PubMed - in process]

Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast.

Proc Natl Acad Sci U S A. 2019 Aug 16;:

Authors: Coutant A, Roper K, Trejo-Banos D, Bouthinon D, Carpenter M, Grzebyta J, Santini G, Soldano H, Elati M, Ramon J, Rouveirol C, Soldatova LN, King RD

Abstract
One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.

PMID: 31420515 [PubMed - as supplied by publisher]

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes.

Yearb Med Inform. 2019 Aug;28(1):152-155

Authors: Dhombres F, Charlet J, Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management

Abstract
OBJECTIVE: To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM).
METHODS: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries.
RESULTS: Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts.
CONCLUSION: In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.

PMID: 31419827 [PubMed - in process]

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.

Yearb Med Inform. 2019 Aug;28(1):140-151

Authors: Bona JP, Prior FW, Zozus MN, Brochhausen M

Abstract
OBJECTIVES: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).
METHODS: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.
RESULTS: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.
CONCLUSIONS: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

PMID: 31419826 [PubMed - in process]

Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.

J Digit Imaging. 2019 Aug 08;:

Authors: Luque EF, Miranda N, Rubin DL, Moreira DA

Abstract
A second opinion about cancer stage is crucial when clinicians assess patient treatment progress. Staging is a process that takes into account description, location, characteristics, and possible metastasis of tumors in a patient. It should follow standards, such as the TNM Classification of Malignant Tumors. However, in clinical practice, the implementation of this process can be tedious and error prone. In order to alleviate these problems, we intend to assist radiologists by providing a second opinion in the evaluation of cancer stage. For doing this, we developed a TNM classifier based on semantic annotations, made by radiologists, using the ePAD tool. It transforms the annotations (stored using the AIM format), using axioms and rules, into AIM4-O ontology instances. From then, it automatically calculates the liver TNM cancer stage. The AIM4-O ontology was developed, as part of this work, to represent annotations in the Web Ontology Language (OWL). A dataset of 51 liver radiology reports with staging data, from NCI's Genomic Data Commons (GDC), were used to evaluate our classifier. When compared with the stages attributed by physicians, the classifier stages had a precision of 85.7% and recall of 81.0%. In addition, 3 radiologists from 2 different institutions manually reviewed a random sample of 4 of the 51 records and agreed with the tool staging. AIM4-O was also evaluated with good results. Our classifier can be integrated into AIM aware imaging tools, such as ePAD, to offer a second opinion about staging as part of the cancer treatment workflow.

PMID: 31396778 [PubMed - as supplied by publisher]

The sound of soft alcohol: Crossmodal associations between interjections and liquor.

PLoS One. 2019;14(8):e0220449

Authors: Winter B, Pérez-Sobrino P, Brown L

Abstract
An increasing number of studies reveal crossmodal correspondences between speech sounds and perceptual features such as shape and size. In this study, we show that an interjection Koreans produce when downing a shot of liquor reliably triggers crossmodal associations in American English, German, Spanish, and Chinese listeners who do not speak Korean. Based on how this sound is used in advertising campaigns for the Korean liquor soju, we derive predictions for different crossmodal associations. Our experiments show that the same speech sound is reliably associated with various perceptual, affective, and social meanings. This demonstrates what we call the 'pluripotentiality' of iconicity, that is, the same speech sound is able to trigger a web of interrelated mental associations across different dimensions. We argue that the specific semantic associations evoked by iconic stimuli depend on the task, with iconic meanings having a 'latent' quality that becomes 'actual' in specific semantic contexts. We outline implications for theories of iconicity and advertising.

PMID: 31393912 [PubMed - in process]

SOCCOMAS: a FAIR web content management system that uses knowledge graphs and that is based on semantic programming.

Database (Oxford). 2019 Jan 01;2019:

Authors: Vogt L, Baum R, Bhatty P, Köhler C, Meid S, Quast B, Grobe P

Abstract
We introduce Semantic Ontology-Controlled application for web Content Management Systems (SOCCOMAS), a development framework for FAIR ('findable', 'accessible', 'interoperable', 'reusable') Semantic Web Content Management Systems (S-WCMSs). Each S-WCMS run by SOCCOMAS has its contents managed through a corresponding knowledge base that stores all data and metadata in the form of semantic knowledge graphs in a Jena tuple store. Automated procedures track provenance, user contributions and detailed change history. Each S-WCMS is accessible via both a graphical user interface (GUI), utilizing the JavaScript framework AngularJS, and a SPARQL endpoint. As a consequence, all data and metadata are maximally findable, accessible, interoperable and reusable and comply with the FAIR Guiding Principles. The source code of SOCCOMAS is written using the Semantic Programming Ontology (SPrO). SPrO consists of commands, attributes and variables, with which one can describe an S-WCMS. We used SPrO to describe all the features and workflows typically required by any S-WCMS and documented these descriptions in a SOCCOMAS source code ontology (SC-Basic). SC-Basic specifies a set of default features, such as provenance tracking and publication life cycle with versioning, which will be available in all S-WCMS run by SOCCOMAS. All features and workflows specific to a particular S-WCMS, however, must be described within an instance source code ontology (INST-SCO), defining, e.g. the function and composition of the GUI, with all its user interactions, the underlying data schemes and representations and all its workflow processes. The combination of descriptions in SC-Basic and a given INST-SCO specify the behavior of an S-WCMS. SOCCOMAS controls this S-WCMS through the Java-based middleware that accompanies SPrO, which functions as an interpreter. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required, thereby seamlessly integrating conventional web page technologies with semantic web technologies. SOCCOMAS and the Java Interpreter are available from (https://github.com/SemanticProgramming).

PMID: 31392324 [PubMed - in process]

Architecture and usability of OntoKeeper, an ontology evaluation tool.

BMC Med Inform Decis Mak. 2019 Aug 08;19(Suppl 4):152

Authors: Amith M, Manion F, Liang C, Harris M, Wang D, He Y, Tao C

Abstract
BACKGROUND: The existing community-wide bodies of biomedical ontologies are known to contain quality and content problems. Past research has revealed various errors related to their semantics and logical structure. Automated tools may help to ease the ontology construction, maintenance, assessment and quality assurance processes. However, there are relatively few tools that exist that can provide this support to knowledge engineers.
METHOD: We introduce OntoKeeper as a web-based tool that can automate quality scoring for ontology developers. We enlisted 5 experienced ontologists to test the tool and then administered the System Usability Scale to measure their assessment.
RESULTS: In this paper, we present usability results from 5 ontologists revealing high system usability of OntoKeeper, and use-cases that demonstrate its capabilities in previous published biomedical ontology research.
CONCLUSION: To the best of our knowledge, OntoKeeper is the first of a few ontology evaluation tools that can help provide ontology evaluation functionality for knowledge engineers with good usability.

PMID: 31391056 [PubMed - in process]

Selected articles from the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018).

BMC Med Inform Decis Mak. 2019 Aug 08;19(Suppl 4):148

Authors: He Z, Bian J, Tao C, Zhang R

Abstract
In this editorial, we first summarize the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018) held on December 3, 2018 in conjunction with the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, and then briefly introduce five research articles included in this supplement issue, covering topics including Data Analytics, Data Visualization, Text Mining, and Ontology Evaluation.

PMID: 31391050 [PubMed - in process]

Using Controlled Vocabularies In Anatomical Terminology: A Case Study With Strumigenys (Hymenoptera: Formicidae).

Arthropod Struct Dev. 2019 Jul 26;:100877

Authors: Silva TSR, Feitosa RM

Abstract
Morphological studies of insects can help us to understand the concomitant or sequential functionality of complex structures and may be used to hypothetize distinct levels of phylogenetic relationship among groups. Traditional morphological works, generally, have encompassed a set of elements, including descriptions of structures and their respective conditions, literature references and images, all combined in a single document. Fast forward to the digital era, it is now possible to release this information simultaneously but also independently as data sets linked to the original publication in an external environment. In order to link data from various fields of knowledge, disseminating morphological information in an open environment, it is important to use tools that enhance interoperability. For example, semantic annotations facilitate the dissemination and retrieval of phenotypic data in digital environments. The integration of semantic (i.e. web-based) components with anatomic treatments can be used to generate a traditional description in natural language along with a set of semantic annotations. The ant genus Strumigenys currently comprises about 840 described species distributed worldwide. In the Neotropical region, almost 200 species are currently known, but it is possible that much of the species' diversity there remains unexplored and undescribed. The morphological diversity in the genus is high, reflecting an extreme generic reclassification that occurred in the late 20th and early 21st centuries. Here we define the anatomical concepts in this highly diverse group of ants using semantic annotations to enrich the anatomical ontologies available online, focusing on the definition of terms through subjacent conceptualization.

PMID: 31357032 [PubMed - as supplied by publisher]