Standard Vocabulary Research Articles

PERFORMA BAHASA DAN KESANTUNAN KOMUNIKASI LISAN PEJABAT LEGISLATIF DAN TOKOH PARTAI TINGKAT KABUPATEN DI SUMATRA BARAT

This study aims to describe: (1) the performance of formal sentence structures in oral communication of district-level legislative and party leaders in West Sumatra, (2) standard vocabulary performance in verbal communication at district level legislative and party leaders in West Sumatra, (3) the principle performance of courtesy in oral communication of district-level legislative and party leaders in West Sumatra; and (4) the performance of the principle of cooperation in the oral communication of district-level legislative and party leaders in West Sumatra. This type of research is qualitative research with descriptive methods. The data of this study are the utterances of legislative and party leaders in West Sumatra in oral communication. The methods and data collection techniques used are the referral method, with advanced techniques, namely the note-taking technique. The technique of validating data is a detailed description technique, namely describing in detail the data to be interpreted. Data analysis methods and techniques are carried out by identifying data, transcribing, entering data into a format, classifying data, interpreting data, and concluding data. Based on the results of research and discussion, it can be concluded the following matters. (1) The performance of the use of formal sentence structures in oral communication of district legislators and party leaders in West Sumatra is quite sufficient (66%) of 150 data. West Sumatra is quite sufficient (66.7%) of 150 data. (3) The performance of the principle of manners in oral communication of legislative officials and party leaders at the district level in West Sumatra, the principle of courtesy is quite sufficient (67.3%) of 150 data. (4) The performance of the principle of cooperation in oral communication of legislative officials and district leaders in West Sumatra is classified as good (81.3%) of 150 data. Keywords: Performance, Oral Communication, Legislative Officials, Party Leaders

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30].

HNS Ontology Using Faceted Approach

The purpose of this research is to develop an ontology with subsequent testing and evaluation, for identifying utility and value. The domain that has been chosen is human nervous system (HNS) disorders. It is hypothesized here that an ontology-based patient records management system is more effective in meeting and addressing complex information needs of health-care personnel. Therefore, this study has been based on the premise that developing an ontology and using it as a component of the search interface in hospital records management systems will lead to more efficient and effective management of health-care. It is proposed here to develop an ontology of the domain of HNS disorders using a standard vocabulary such as MeSH or SNOMED CT. The principal classes of an ontology include facet analysis for arranging concepts based on their common characteristics to build mutually exclusive classes. We combine faceted theory with description logic, which helps us to better query and retrieve data by implementing an ontological model. Protégé 5.2.0 was used as ontology editor. The use of ontologies for domain modelling will be of acute help to doctors for searching patient records. In this paper we show how the faceted approach helps us to build a flexible model and retrieve better information. We use the medical domain as a case study to show examples and implementation.

Toward Launching Electronic Terminology Services in Radiological Technology-The History and Transition of Activities for Building Standard Vocabularies in JSRT

Toward Launching Electronic Terminology Services in Radiological Technology-The History and Transition of Activities for Building Standard Vocabularies in JSRT

Integration of Postcoordination Content into a Clinical Interface Terminology to Support Administrative Coding

Background Clinical interface terminologies (CITs) consist of terms designed for clinical documentation and, through mappings to standardized vocabularies, to support secondary uses of patient data, including clinical decision support, quality measurement, and billing for health care services. The latter purpose requires maps to administrative coding systems, such as the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), for diagnoses in the United States. Objectives The transition from ICD-9-CM to ICD-10-CM posed a challenge to CIT users due to the substantially increased details in ICD-10-CM. To address this, we developed a content layer within a CIT that provides postcoordination prompts for the details required for accurate ICD-10-CM coding. Methods We developed content to support prompting for and capture of additional information specified by the user in a single, clinically relevant term that is added to the patient's record, and whose mapping to other coding systems (like Systematized Nomenclature of Medicine—Clinical Terms [SNOMED CT]) reflects the details added during postcoordination. We worked with clinical information system developers to incorporate this into user interfaces, and with end-users to refine the design. Results While the prompts were designed around the precoordinated elements implicit in ICD-10-CM, irregularities in ICD-10-CM required some additional design measures, such as providing postcoordination options that interpolate gaps in ICD-10-CM to avoid user confusion. The system we describe has been implemented by ∼30,000 health care provider organizations, with content that covers the vast majority of encounter diagnoses. User feedback has been largely positive, though concerns have been raised about expanding postcoordination content beyond that required for ICD-10-CM coding. Conclusion We have demonstrated the design and development of what, to our knowledge, is the first system that uses postcoordination to capture ICD-10-CM-relevant details in a CIT while also reflecting the details added by the user in maps to other vocabularies.

A Lexicon for Descriptive Sensory Evaluation of Blended Tea.

A lexicon provides standardized vocabulary that facilitates communication among various objects. We developed a lexicon to describe the sensory flavor and aroma characteristics of fermented tea and blended tea. The 37 sensory attributes of blended tea were identified, defined, and referenced by six highly trained panelists. The lexicon included 24 flavor attributes and 13 aroma attributes. Ingredients in the blended tea included fermented tea, rose petal, lavender, hibiscus, chamomile, peppermint, lemongrass, rosemary, pandanus, and osmanthus. Most of the lexicon attributes were derived from the constituents found in the tea: brown, spicy, cooling, earthy, nutty, sweet, and bitter. Also, attributes derived from the characteristics of the ingredients were included. The lexicon developed in this study can help more accurately describe the flavors and aromas of tea containing fermented tea and herbs.

Jurisdictional Culture and Memory Digitization of the “Government of Justice.” Data Modeling and Digital Approach for the Legal History of Ibero-America

Can a machine retrieve the cultural meaning from a corpus of sources? This article addresses the scope and restrictions that digitization, transcription, and data modeling represents for machine-mediated readings of legal, historical records, particularly those derived from the cultural context of Hispanic empire. It compares the dichotomy between the ambiguous language of ancient regime legal texts and the unambiguity required by machinereadable files. Besides, is problematize the corporal reading and the strategy of distant-reading and visualizations as a model for interpretation of vast bulk of textual data. We propose a strategy for segmentation and data modelling for the approach the textual logic of ancient regime legal records based on their hierarchization, interrelation with nonjudiciary sources (theological, historical, philosophical, etc.), its internal segmentation, the non-linear logic of the normative, and the authoritative requirements of compilations and relevant legal works. Its conclude that the advantages of automation are attached to the ability to manipulate files without distorting the original meaning of the texts, therefore, it proposes the necessity to develop standardized vocabularies that help to avoid anachronistic approaches regarding Modern Age legal sources.

Politizando las ecologías políticas urbanas

El artículo parte de la premisa que es de vital importancia reconocer la rápida tasa de urbanización planetaria como el principal impulsor del cambio ambiental y la creación del Antropoceno. Por lo tanto, este documento no se ocupa mayormente de la cuestión de la naturaleza en la ciudad, como de la urbanización de la naturaleza, entendida como el proceso a través del cual todas las formas de la naturaleza son socialmente movilizadas, incorporadas económica y físicamente, metabolizadas / transformadas, para apoyar el proceso de urbanización. Se argumenta que las transformaciones de nuestro destino urbano y de nuestra naturaleza, están inevitablemente ligadas a una simbiosis metabólica íntima e intensificadora – pero muy contenciosa – caracterizada por patrones socio- ecológicos extraordinariamente desiguales. La configuración de esta relación metabólica urbana ahora se ha elevado a la dignidad de la preocupación pública mundial, y una búsqueda febril de todo tipo de remedios “sostenibles” eco-profilácticos ha entrado en el vocabulario estándar de los actores gubernamentales y privados. La naturaleza como marco de condicionamiento externo para la vida urbana ha llegado a su fin. La inauguración antropocénica de una naturaleza socio-física, histórica y globalmente urbanizada, obliga a una profunda reconsideración y reescritura de la naturaleza y la urbanización en términos políticos. La cuestión, ya no se trata de llevar los problemas ambientales al dominio de la política urbana, sino de cómo llevar lo político al ambiente urbano.

Documenting social determinants of health-related clinical activities using standardized medical vocabularies.

ObjectivesGrowing recognition that health is shaped by social and economic circumstances has resulted in a rapidly expanding set of clinical activities related to identifying, diagnosing, and intervening around patients’ social risks in the context of health care delivery. The objective of this exploratory analysis was to identify existing documentation tools in common US medical coding systems reflecting these emerging clinical practices to improve patients’ social health.Materials and MethodsWe identified 20 social determinants of health (SDH)-related domains used in 6 published social health assessment tools. We then used medical vocabulary search engines to conduct three independent searches for codes related to these 20 domains included in common medical coding systems (LOINC, SNOMED CT, ICD-10-CM, and CPT). Each of the 3 searches focused on one of three clinical activities: Screening, Assessment/Diagnosis, and Treatment/Intervention.ResultsWe found at least 1 social Screening code for 18 of the 20 SDH domains, 686 social risk Assessment/Diagnosis codes, and 243 Treatment/Intervention codes. Fourteen SDH domains (70%) had codes across all 3 clinical activity areas.DiscussionOur exploratory analysis revealed 1095 existing codes in common medical coding vocabularies that can facilitate documentation of social health-related clinical activities. Despite a large absolute number of codes, there are addressable gaps in the capacity of current medical vocabularies to document specific social risk factor screening, diagnosis, and interventions activities.ConclusionsFindings from this analysis should help inform efforts both to develop a comprehensive set of SDH codes and ultimately to improve documentation of SDH-related activities in clinical settings.

Analysis of standard vocabulary use of the open government data: the case of the public data portal of Korea

Open data is an important element in a variety of new industries, such as artificial intelligence and smart cities. While the South Korean government is continuously releasing new data on the public data portal, it is limited in the accomplishment of the goals such as job creation and the new economic leaps expected by the Korean government. From a data point of view, this limitation is due to a lack of data that users require and a lot of low-quality data to use. This paper analyses standard terms used in public data. The findings of this study reveals that standard vocabularies established by the government require updates to reflect the nature of public data, and the relevant laws and guidelines need to be revised.

Preliminary exploration of survival analysis using the OHDSI common data model: a case study of intrahepatic cholangiocarcinoma

BackgroundData heterogeneity is a common phenomenon related to the secondary use of electronic health records (EHR) data from different sources. The Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM) organizes healthcare data into standard data structures using concepts that are explicitly and formally specified through standard vocabularies, thereby facilitating large-scale analysis. The objective of this study is to design, develop, and evaluate generic survival analysis routines built using the OHDSI CDM.MethodsWe used intrahepatic cholangiocarcinoma (ICC) patient data to implement CDM-based survival analysis methods. Our methods comprise the following modules: 1) Mapping local terms to standard OHDSI concepts. The analytical expression of variables and values related to demographic characteristics, medical history, smoking status, laboratory results, and tumor feature data. These data were mapped to standard OHDSI concepts through a manual analysis; 2) Loading patient data into the CDM using the concept mappings; 3) Developing an R interface that supports the portable survival analysis on top of OHDSI CDM, and comparing the CDM-based analysis results with those using traditional statistical analysis methods.ResultsOur dataset contained 346 patients diagnosed with ICC. The collected clinical data contains 115 variables, of which 75 variables were mapped to the OHDSI concepts. These concepts mainly belong to four domains: condition, observation, measurement, and procedure. The corresponding standard concepts are scattered in six vocabularies: ICD10CM, ICD10PCS, SNOMED, LOINC, NDFRT, and READ. We loaded a total of 25,950 patient data records into the OHDSI CDM database. However, 40 variables failed to map to the OHDSI CDM as they mostly belong to imaging data and pathological data.ConclusionsOur study demonstrates that conducting survival analysis using the OHDSI CDM is feasible and can produce reusable analysis routines. However, challenges to be overcome include 1) semantic loss caused by inaccurate mapping and value normalization; 2) incomplete OHDSI vocabularies describing imaging data, pathological data, and modular data representation.

A Study on Standardization of Concept Vocabulary in Korean Language Textbook

A Study on Standardization of Concept Vocabulary in Korean Language Textbook

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

Location Information Quality: A Review.

The quality of location information is an important factor for location-based services (LBSs). In the literature, the quality of location information has been defined in different ways based on varying sets of aspects. The objectives of this paper are to review existing literature discussing location information quality and to provide a consistent framework for describing and dealing with location information quality. In particular, we review existing literature on different aspects of location information quality and on factors that affect location sensing technologies (and thus location information quality). Based on this review, we also propose a simple model for describing location information quality and a classification of the strategies for dealing with variations in the quality of location information. Designers of location sensing systems can use this model as a standard vocabulary for describing the quality of location information. The classification of strategies can be used by developers of LBSs apps to design alternative strategies for dealing with location information quality on three levels: sensor-level, algorithm-level, and application-level, which are aligned with the Location Stack model.

Stern, gwiazda or star: Screening receptive vocabulary skills across languages in monolingual and bilingual German–Polish or German–Turkish children using a tablet application

There is a need to provide bilingual assessments and reference data to identify those who struggle to acquire their heritage language (L1) or the language spoken in the country of residence (L2). However, bilingual assessments and data are still sparse. Therefore, the aim was to use a tablet application to screen receptive vocabulary in different languages and discuss this data in the context of lexical acquisition theories. Forty-four monolingual German, 15 bilingual German–Polish and 21 German–Turkish-speaking children aged between 3;5 (3 years and 5 months) and 6;1 were assessed. All children completed the German version of the Receptive Vocabulary Screener (RVS), a tablet application testing 20 nouns and 20 verbs, and two standardized vocabulary sub-tests. Additionally, the bilingual children completed the Turkish or Polish version of the RVS. Internal consistency showed that the RVS is a reliable tool for research purposes and validity was confirmed by significant and moderate to strong correlations with the two standardized vocabulary sub-tests. Monolingual children outperformed bilingual children when performance comparisons were solely based on the German items. However, group differences were not significant when total vocabulary was used, i.e. the number of words bilingual children named across both versions. For bilingual children, L1 and L2 scores did not differ, i.e. they showed a similar performance on the L1/L2 subtest. Paternal education, but not maternal education, was significantly correlated to vocabulary scores. Children with more translation equivalents, i.e. words they know in both languages, and with a higher level of language proficiency showed higher scores on the screener. The app provided a valuable opportunity to assess lexical knowledge across different languages. Results indicated that total vocabulary and translation equivalents must be considered to evaluate bilingual children’s lexical knowledge.

Project Tycho 2.0: a repository to improve the integration and reuse of data for global population health.

ObjectiveIn 2013, we released Project Tycho, an open-access database comprising 3.6 million counts of infectious disease cases and deaths reported for over a century by public health surveillance in the United States. Our objective is to describe how Project Tycho version 1 (v1) data has been used to create new knowledge and technology and to present improvements made in the newly released version 2.0 (v2).Materials and MethodsWe analyzed our user database and conducted online searches to analyze the use of Project Tycho v1 data. For v2, we added new US data and dengue data for other countries, and grouped data into 360 datasets, each with a digital object identifier and rich metadata. In addition, we used standard vocabularies to encode data where possible, improving compliance with FAIR (findable, accessible, interoperable, reusable) guiding principles for data management.ResultsSince release, 3174 people have registered to use Project Tycho data, leading to 18 new peer-reviewed papers and 27 other creative works, such as conference papers, student theses, and software applications. Project Tycho v2 comprises 5.7 million counts of infectious diseases in the United States and of dengue-related conditions in 98 additional countries.DiscussionProject Tycho v2 contributes to improving FAIR compliance of global health data, but more work is needed to develop community-accepted standard representations for global health data.ConclusionFAIR principles are a valuable guide for improving the integration and reuse of data in global health to improve disease control and save lives.

Building word knowledge, learning strategies, and metacognition with the Word-Knowledge e-Book

Many children fail to comprehend what they read because they do not monitor their understanding, which requires making accurate judgements of what they know and then employing repair strategies when comprehension fails. Relying on research from learning science and cognitive and developmental psychology, we developed the Word Knowledge e-Book (WKe-Book) to improve children's calibration of their word knowledge, strategy use, and word knowledge overall; skills which are associated with reading comprehension. The WKe-Book, which is read on a tablet computer, is a choose-your-own adventure book where choices require choosing between two rare words (e.g., cogitate vs. procrastinate). Depending on the word chosen, the story follows a different plot. There are also embedded comprehension questions where students receive immediate feedback with consequences for incorrect answers, such as being sent back to reread a few pages. In a randomized controlled trial, we tested whether students (N = 603 in 25 third through fifth grade classrooms in Arizona in the US) reading the WKe-Book would demonstrate improved word knowledge, strategy use, and word knowledge calibration. Classrooms were randomly assigned to read the WKe-Book immediately (treatment) or later (delayed-treatment control), and within classrooms, students were randomly assigned to either participate in a 15-min weekly book club (book club treatment) or to read the WKe-Book independently with no book club (no book club control). Results revealed a significant treatment effect of the WKe-Book on students' word knowledge, word knowledge calibration, and strategy use, which predicted student performance on standardized reading comprehension and vocabulary measures. The effects were greater for students who participated in weekly book clubs compared to students in the no book club control. These findings suggest that the affordances offered by technology, which are unavailable in paper-based books, can support students' development of metacognition, including word knowledge calibration, strategy use, and word learning skills.

Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.

Identifying the genes and proteins associated with a biological process or disease is a central goal of the biomedical research enterprise. However, relatively few systematic approaches are available that provide objective evaluation of the genes or proteins known to be important to a research topic, and hence researchers often rely on subjective evaluation of domain experts and laborious manual literature review. Computational bibliometric analysis, in conjunction with text mining and data curation, attempts to automate this process and return prioritized proteins in any given research topic. We describe here a method to identify and rank protein-topic relationships by calculating the semantic similarity between a protein and a query term in the biomerical literature while adjusting for the impact and immediacy of associated research articles. We term the calculated metric the weighted copublication distance (WCD) and show that it compares well to related approaches in predicting benchmark protein lists in multiple biological processes. We used WCD to extract prioritized "popular proteins" across multiple cell types, subanatomical regions, and standardized vocabularies containing over 20 000 human disease terms. The collection of protein-disease associations across the resulting human "diseasome" supports data analytical workflows to perform reverse protein-to-disease queries and functional annotation of experimental protein lists. We envision that the described improvement to the popular proteins strategy will be useful for annotating protein lists and guiding method development efforts as well as generating new hypotheses on understudied disease proteins using bibliometric information.

Imaging Biomarker Ontology (IBO): A Biomedical Ontology to Annotate and Share Imaging Biomarker Data

Imaging biomarkers refer to radiological measurements that characterize biological processes of imaged subjects and help clinicians particularly in the assessment of therapeutic responses and the early prediction of pathologies. Several imaging features (size of a lesion, volume of a tumor, blood perfusion in a specific anatomical region, anisotropic water diffusion in a particular tissue region, etc.) are quantified and reported in the clinical practice. The growth of the number of research studies addressing imaging biomarkers and the increasing use of these measurements in the radiological routine necessitates the use of semantic research tools. The use of semantic technologies will enable to efficiently retrieve imaging-related data and to enhance the interoperability in the biomedical field. While many efforts have been conducted regarding the definition of a standardized vocabulary to support the sharing of the imaging biomarker knowledge, the definition of the term “imaging biomarker” stills inconsistent. In this paper, we introduce our motivation for semantically describing this concept and we outline shortcomings of the state-of-the-art methods. Here, we propose a semantic representation of the imaging biomarker concept that is based on the articulation of its three main semantic axes, namely the measured quality, the measurement tool and the decision tool. The developed ontology is called the Imaging Biomarker Ontology (IBO) and uses existing biomedical ontologies. A preliminary use case is studied to illustrate the utility of IBO in annotating quantitative and qualitative imaging data from the TCGA (The Cancer Genome Atlas) collection.

Data linkage in medical science using the resource description framework: the AVERT model

There is an ongoing challenge as to how best manage and understand 'big data' in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This "AVERT model" provides a framework for converting multiple standalone files of various formats, from both clinicaland environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.