Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible.

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.

Translation of Ontological Concepts from English into German Using Commercial Translation Software and Expert Evaluation.

Medical ontologies are mostly available in English. This presents a language barrier that is a limitation in research and automated processing of patient data. The manual translation of ontologies is complex and time-consuming. However, there are commercial translation tools that have shown promising results in the field of medical terminology translation. The aim of this study is to translate selected terms of the Human Phenotype Ontology (HPO) from English into German using commercial translators. Six medical experts evaluated the translation candidates in an iterative process. The results show commercial translators, with DeepL in the lead, provide translations that are positively evaluated by experts. With a broader study scope and additional optimization techniques, commercial translators could support and facilitate the process of translating medical ontologies.

OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.

IMPORTANCE: The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization. MATERIALS AND METHODS: We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts. RESULTS: The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting. DISCUSSION AND CONCLUSION: OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

Getting More Out of Clinical Documentation: Can Clinical Dashboards Yield Clinically Useful Information?

This study investigated coded data retrieved from clinical dashboards, which are decision-support tools that include a graphical display of clinical progress and clinical activities. Data were extracted from clinical dashboards representing 256 youth (M age = 11.9) from 128 practitioners who were trained in the Managing and Adapting Practice (MAP) system (Chorpita & Daleiden in BF Chorpita EL Daleiden 2014 Structuring the collaboration of science and service in pursuit of a shared vision. 43(2):323 338. 2014, Chorpita & Daleiden in BF Chorpita EL Daleiden 2018 Coordinated strategic action: Aspiring to wisdom in mental health service systems. 25(4):e12264. 2018) in 55 agencies across 5 regional mental health systems. Practitioners labeled up to 35 fields (i.e., descriptions of clinical activities), with the options of drawing from a controlled vocabulary or writing in a client-specific activity. Practitioners then noted when certain activities occurred during the episode of care. Fields from the extracted data were coded and reliability was assessed for Field Type, Practice Element Type, Target Area, and Audience (e.g., Caregiver Psychoeducation: Anxiety would be coded as Field Type = Practice Element; Practice Element Type = Psychoeducation; Target Area = Anxiety; Audience = Caregiver). Coders demonstrated moderate to almost perfect interrater reliability. On average, practitioners recorded two activities per session, and clients had 10 unique activities across all their sessions. Results from multilevel models showed that clinical activity characteristics and sessions accounted for the most variance in the occurrence, recurrence, and co-occurrence of clinical activities, with relatively less variance accounted for by practitioners, clients, and regional systems. Findings are consistent with patterns of practice reported in other studies and suggest that clinical dashboards may be a useful source of clinical information. More generally, the use of a controlled vocabulary for clinical activities appears to increase the retrievability and actionability of healthcare information and thus sets the stage for advancing the utility of clinical documentation.

Development of a 3-Step theory of suicide ontology to facilitate 3ST factor extraction from clinical progress notes.

OBJECTIVE: Suicide risk prediction algorithms at the Veterans Health Administration (VHA) do not include predictors based on the 3-Step Theory of suicide (3ST), which builds on hopelessness, psychological pain, connectedness, and capacity for suicide. These four factors are not available from structured fields in VHA electronic health records, but they are found in unstructured clinical text. An ontology and controlled vocabulary that maps psychosocial and behavioral terms to these factors does not exist. The objectives of this study were 1) to develop an ontology with a controlled vocabulary of terms that map onto classes that represent the 3ST factors as identified within electronic clinical progress notes, and 2) to determine the accuracy of automated extractions based on terms in the controlled vocabulary. METHODS: A team of four annotators did linguistic annotation of 30,000 clinical progress notes from 231 Veterans in VHA electronic health records who attempted suicide or who died by suicide for terms relating to the 3ST factors. Annotation involved manually assigning a label to words or phrases that indicated presence or absence of the factor (polarity). These words and phrases were entered into a controlled vocabulary that was then used by our computational system to tag 14 million clinical progress notes from Veterans who attempted or died by suicide after 2013. Tagged text was extracted and machine-labelled for presence or absence of the 3ST factors. Accuracy of these machine-labels was determined for 1000 randomly selected extractions for each factor against a ground truth created by our annotators. RESULTS: Linguistic annotation identified 8486 terms that related to 33 subclasses across the four factors and polarities. Precision of machine-labeled extractions ranged from 0.73 to 1.00 for most factor-polarity combinations, whereas recall was somewhat lower 0.65-0.91. CONCLUSION: The ontology that was developed consists of classes that represent each of the four 3ST factors, subclasses, relationships, and terms that map onto those classes which are stored in a controlled vocabulary (https://bioportal.bioontology.org/ontologies/THREE-ST). The use case that we present shows how scores based on clinical notes tagged for terms in the controlled vocabulary capture meaningful change in the 3ST factors during weeks preceding a suicidal event.

Machine translation of standardised medical terminology using natural language processing: A scoping review.

Standardised medical terminologies are used to ensure accurate and consistent communication of information and to facilitate data exchange. Currently, many terminologies are only available in English, which hinders international research and automated processing of medical data. Natural language processing (NLP) and Machine Translation (MT) methods can be used to automatically translate these terms. This scoping review examines the research on automated translation of standardised medical terminology. A search was performed in PubMed and Web of Science and results were screened for eligibility by title and abstract as well as full text screening. In addition to bibliographic data, the following data items were considered: 'terminology considered', 'terms considered', 'source language', 'target language', 'translation type', 'NLP technique', 'NLP system', 'machine translation system', 'data source' and 'translation quality'. The results showed that the most frequently translated terminology is SNOMED CT (39.1%), followed by MeSH (13%), ICD (13%) and UMLS (8.7%). The most common source language is English (55.9%), and the most common target language is German (41.2%). Translation methods are often based on Statistical Machine Translation (SMT) (41.7%) and, more recently, Neural Machine Translation (NMT) (30.6%), but can also be combined with various MT methods. Commercial translators such as Google Translate (36.4%) and automatic validation methods such as BLEU (22.2%) are frequently used tools for translation and subsequent validation.

Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices.

OBJECTIVE: Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies. MATERIALS AND METHODS: To retrieve articles from PubMed that addressed patient/consumer engagement with wearables, we developed a search strategy of textwords and Medical Subject Headings (MeSH). To refine our methodology, we used a random sample of 200 articles from 2016 to 2018. A descriptive analysis of articles (N = 2522) from 2019 identified 308 (12.2%) CHI-related articles, for which we characterized their assigned terminology. We visualized the 100 most frequent terms assigned to the articles from MeSH, author keywords, CINAHL, and Engineering Databases (Compendex and Inspec together). We assessed the overlap of CHI terms among sources and evaluated terms related to consumer engagement. RESULTS: The 308 articles were published in 181 journals, more in health journals (82%) than informatics (11%). Only 44% were indexed with the MeSH term "wearable electronic devices." Author keywords were common (91%) but rarely represented consumer engagement with device data, eg, self-monitoring (n = 12, 0.7%) or self-management (n = 9, 0.5%). Only 10 articles (3%) had terminology from all sources (authors, PubMed, CINAHL, Compendex, and Inspec). DISCUSSION: Our main finding was that consumer engagement was not well represented in health and engineering database thesauri. CONCLUSIONS: Authors of CHI studies should indicate consumer/patient engagement and the specific technology investigated in titles, abstracts, and author keywords to facilitate discovery by readers and expand vocabularies and indexing.

Definition, Composition, and Harmonization of Core Datasets Within the German Center for Lung Research.

Core datasets are the composition of essential data items for a certain research scope. As they state commonalities between heterogeneous data collections, they serve as a basis for cross-site and cross-disease research. Therefore, researchers at the national and international levels have addressed the problem of missing core datasets. The German Center for Lung Research (DZL) comprises five sites and eight disease areas and aims to gain further scientific knowledge by continuously promoting collaborations. In this study, we elaborated a methodology for defining core datasets in the field of lung health science. Additionally, through support of domain experts, we have utilized our method and compiled core datasets for each DZL disease area and a general core dataset for lung research. All included data items were annotated with metadata and where possible they were assigned references to international classification systems. Our findings will support future scientific collaborations and meaningful data collections.

Use of the clinical care classification in South Korean nursing practice: Challenges and opportunities.

BACKGROUND AND OBJECTIVES: A government-driven standardization of nursing terminology including the Clinical Care Classification (CCC) was endorsed in South Korea in 2015, but the number of hospitals who have adopted this standard terminology remains unknown. This study aimed to determine the CCC awareness, adoption, and utilization statuses and its association with patient experience in South Korea. DESIGN, SETTING, AND PARTICIPANTS: A nationwide telephone survey was conducted from January 13 to February 12, 2022 among 217 tertiary and secondary hospitals participating in the health information exchange network. The survey questionnaire included 22 items in 3 categories: current status of electronic nursing records, awareness and adoption of standard terminology, and open-ended questions regarding standard usage and dissemination. General characteristics and experience scores of the patients of the surveyed hospitals were collected from the publicly available data sources. Data analysis was performed using descriptive statistics, t-test, and generalized linear regression. MAIN OUTCOMES AND MEASURES: The rates of awareness and adoption in hospitals to the nursing terminology standard of the CCC were calculated, and the current status of electronic nursing records used in practice was examined. The relationships between CCC awareness and the characteristics of hospitals in their patient experiences of health services were also identified. RESULTS: The survey response rate was 24.9 % (54/217). Two out of three hospitals (68.5 %) were aware of the CCC. These hospitals had 800 beds or more, and higher scores for patient experience. CCC awareness was significantly related to increases in the overall scores for patient experiences (t = 2.70, p =.0103), but no significance with sub-score for nursing service (t = 1.23, p =.1594). CONCLUSIONS: With a high adoption rate of electronic medical record systems, two-third hospitals acknowledged their CCC awareness, but were still lagged in adoption and usage of it in practice with operational challenges. The CCC awareness has potential relationships with positive patient experience.

Patient safety classifications, taxonomies and ontologies: A systematic review on development and evaluation methodologies.

INTRODUCTION: Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using a variety of methods. The purpose of this review was to discuss and analyze the methodologies for developing and evaluating patient safety classifications/ontologies. METHODS: Studies that developed or evaluated patient safety classifications, terminologies, taxonomies, or ontologies were searched through Google Scholar, Google search engines, National Center for Biomedical Ontology (NCBO) BioPortal, Open Biological and Biomedical Ontology (OBO) Foundry and World Health Organization (WHO) websites and Scopus, Web of Science, PubMed, and Science Direct. We updated our search on 30 February 2021 and included all studies published until the end of 2020. Studies that developed or evaluated classifications only for patient safety and provided information on how they were developed or evaluated were included. Systems with covered patient safety terms (such as ICD-10) but are not specifically developed for patient safety were excluded. The quality and the risk of bias of studies were not assessed because all methodologies and criteria were intended to be covered. In addition, we analyzed the data through descriptive narrative synthesis and compared and classified the development and evaluation methods and evaluation criteria according to available development and evaluation approaches for biomedical ontologies. RESULTS: We identified 84 articles that met all of the inclusion criteria, resulting in 70 classifications/ontologies, nine of which were for the general medical domain. The most papers were published in 2010 and 2011, with 8 and 7 papers, respectively. The United States (50) and Australia (23) have the most studies. The most commonly used methods for developing classifications/ontologies included the use of existing systems (for expanding or mapping) (44) and qualitative analysis of event reports (39). The most common evaluation methods were coding or classifying some safety report samples (25), quantitative analysis of incidents based on the developed classification (24), and consensus among physicians (16). The most commonly applied evaluation criteria were reliability (27), content and face validity (9), comprehensiveness (6), usability (5), linguistic clarity (5), and impact (4), respectively. CONCLUSIONS: Because of the weaknesses and strengths of the development/evaluation methods, it is advised that more than one method for development or evaluation, as well as evaluation criteria, should be used. To organize the processes of developing classification/ontologies, well-established approaches such as Methontology are recommended. The most prevalent evaluation methods applied in this domain are well fitted to the biomedical ontology evaluation methods, but it is also advised to apply some evaluation approaches such as logic, rules, and Natural language processing (NLP) based in combination with other evaluation approaches. This research can assist domain researchers in developing or evaluating domain ontologies using more complete methodologies. There is also a lack of reporting consistency in the literature and same methods or criteria were reported with different terminologies.

Development and Implementation of a Standard Format for Clinical Laboratory Test Results.

OBJECTIVES: Surprisingly, laboratory results, the principal output of clinical laboratories, are not standardized. Thus, laboratories frequently report results with identical meaning in different formats. For example, laboratories report a positive pregnancy test as "+," "P," or "Positive." To assess the feasibility of a widespread implementation of a result standard, we (1) developed a standard result format for common laboratory tests and (2) implemented a feedback system for clinical laboratories to view their unstandardized results. METHODS: In the largest integrated health care system in America, 130 facilities had the opportunity to collaboratively develop the standard. For 15 weeks, clinical laboratories received a weekly report of their unstandardized results. At the study's conclusion, laboratories were compared with themselves and their peers by metrics that reflected their unstandardized results. RESULTS: We rereviewed 156 million test results and observed a 51% decline in the rate of unstandardized results. The number of facilities with fewer than 23 unstandardized results per 100,000 (Six Sigma σ > 5) increased by 58% (52 to 82 facilities; ß = 1.79; P < .001). CONCLUSIONS: This study demonstrated significant improvement in the standardization of clinical laboratory results in a relatively short time. The laboratory community should create and promulgate a standardized result format.

IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset.

Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15 years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset.

Trends in Controlled Vocabulary and Health Equity.

Medical librarians collaborate with physicians and other healthcare professionals to improve the quality and accessibility of medical information, which includes assembling the best evidence to advance health equality through teaching and research. This column brings together brief cases highlighting the experiences and perspectives of medical librarians, educators, and healthcare professionals using their organizational, pedagogical, and information-analysis skills to advance health equality indexing.

The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment.

The need for assembled existing and new toxicity data has accelerated as the amount of chemicals introduced into commerce continues to grow and regulatory mandates require safety assessments for a greater number of chemicals. To address this evolving need, the ECOTOXicology Knowledgebase (ECOTOX) was developed starting in the 1980s and is currently the world's largest compilation of curated ecotoxicity data, providing support for assessments of chemical safety and ecological research through systematic and transparent literature review procedures. The recently released version of ECOTOX (Ver 5, www.epa.gov/ecotox) provides single-chemical ecotoxicity data for over 12,000 chemicals and ecological species with over one million test results from over 50,000 references. Presented is an overview of ECOTOX, detailing the literature review and data curation processes within the context of current systematic review practices and discussing how recent updates improve the accessibility and reusability of data to support the assessment, management, and research of environmental chemicals. Relevant and acceptable toxicity results are identified from studies in the scientific literature, with pertinent methodological details and results extracted following well-established controlled vocabularies and newly extracted toxicity data added quarterly to the public website. Release of ECOTOX, Ver 5, included an entirely redesigned user interface with enhanced data queries and retrieval options, visualizations to aid in data exploration, customizable outputs for export and use in external applications, and interoperability with chemical and toxicity databases and tools. This is a reliable source of curated ecological toxicity data for chemical assessments and research and continues to evolve with accessible and transparent state-of-the-art practices in literature data curation and increased interoperability to other relevant resources. Environ Toxicol Chem 2022;41:1520-1539. © 2022 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Modelo ontológico para sistematização da assistência de enfermagem na perspectiva da teoria da complexidade / Ontological model for the systematization of nursing care from the perspective of complexity theory

Resumo: Introdução: A Sistematização da Assistência de Enfermagem corresponde à organização do trabalho quanto ao método, pessoal e instrumentos, vislumbrando operacionalizar o processo de enfermagem. Porém, há limitação da compreensão semântica do seu significado, conhecimento, operacionalização dos seus componentes e da contribuição para prática profissional e Ciência da Enfermagem. Objetivo: analisar, sob a ótica da Teoria da Complexidade, a construção de um modelo ontológico sobre Sistematização da Assistência de Enfermagem como tecnologia de apoio à organização da prática profissional do enfermeiro. Método: estudo qualitativo e exploratório, em três etapas. Primeiramente, construiu-se um mapa conceitual baseado nas sete etapas apresentadas por Cañas, Novak, Reiska (2015), almejando identificar conceitos, estrutura, processos e operação da Sistematização da Assistência de Enfermagem, à luz da Teoria da Complexidade. Organizou-se e representou-se o conhecimento com apoio do software CMap Tools. A segunda etapa compôs-se de entrevistas semiestruturadas, entre maio e dezembro de 2020, com 17 enfermeiros, dos quais nove eram do Grupo de Trabalho da Sistematização da Prática de Enfermagem da Comissão Mista da Associação Brasileira de Enfermagem e Conselho Regional de Enfermagem-PR e oito da Comissão Permanente de Sistematização da Prática de Enfermagem, nomeada pela Associação Brasileira de Enfermagem. Empregou-se a Análise de Conteúdo Temática, apoiada no software MAXQDA. Na terceira etapa, modelou-se a representação de uma ontologia sobre a Sistematização da Assistência de Enfermagem, baseada no guia interativo Ontology Development 101 apoiada pelo software Protégé (versão 5.5.0), a partir do mapa conceitual e das entrevistas. Resultados: identificou-se inconsistência semântica e de correlações, retratando a complexidade dos componentes da Sistematização da Assistência de Enfermagem, com fragmentos mecanicistas. No mapa conceitual, elaboraram-se três camadas conceituais. Organizaram-se os conceitos de acordo com a proposta conceitual da Sistematização da Assistência de Enfermagem prevista em sua principal legislação e posteriormente foram ampliados. Desta análise, procedeu-se ao agrupamento por temáticas: Sistematização da Assistência de Enfermagem; Ações de Enfermagem; Ações da Gestão do Cuidado; Ações de Gestão do Serviço de Enfermagem; Ações para Aplicação dos Cuidados; Ações para Aplicação no Serviço de Enfermagem; Fundamentos; Competências; Instrumentos; Normativas e Pessoal. Das entrevistas, emergiram 863 unidades de registro e seis categorias: Significado de Sistematização da Assistência de Enfermagem, com três subcategorias primárias; Construção Histórica do Conceito de Sistematização da Assistência de Enfermagem, com quatro subcategorias primárias; Ensino e Aprendizagem; Pesquisa da Enfermagem; Implicações Prática e Concretização da Sistematização da Assistência de Enfermagem. Identificaram-se 156 conceitos relevantes para modelagem da ontologia, utilizando-se da "metodologia 101", objetivando representar o conhecimento do domínio Sistematização da Assistência de Enfermagem. Considerações finais: a ontologia sobre Sistematização da Assistência de Enfermagem ancorada na Teoria da Complexidade permitiu um novo olhar sobre os fenômenos, os quais devem ser desenvolvidos, revistos e ressignificados. Acredita-se que esta ontologia facilite a representação formal do conhecimento sobre Sistematização da Assistência de Enfermagem, afirmando-a enquanto área de conhecimento representativo, fortalecendo sua identidade, significado unívoco, organização, compartilhamento de saberes e de informação. Ademais, pode favorecer difusão de vocabulário comum, contribuindo com a prática profissional de enfermeiros.

Abstract: Introduction: the Systematization of Nursing Care is the work organization according to the method, personnel and instruments, which glimpses to operationalize the nursing process. However, there is a limitation in the semantic understanding of its meaning, knowledge, and operationalization of its components and the contribution to the practice and science of nursing. Objective: to analyze, from the perspective of Complexity Theory, the process of building an ontological model on Systematization of Nursing Care as a technology to support the organization of professional nursing practice. Method: qualitative and exploratory study, in three stages. Firstly, a conceptual map was built based on the seven stages presented by Cañas, Novak, Reiska (2015), aiming to identify concepts, structure, processes and operation of the Systematization of Nursing Care, in light of the complexity, anchored in the related literature. Knowledge was organized and represented with the support of CMap Tools software. The second stage consisted of semi-structured interviews, between May and December 2020, done with 17 professionals, of whom nine from the Working Group on the Systematization of Nursing Practice of the Mixed Commission of the Brazilian Nursing Association and Regional Nursing Council-PR and eight from the Permanent Commission for the Systematization of Nursing Practice, appointed by the Association. Thematic Content Analysis was used, supported by the MAXQDA software. In the third stage, the representation of ontology on the Systematization of Nursing Care was modeled, based on the interactive guide Ontology Development 101 supported by the software Protégé (version 5.5.0), from the conceptual map and the interviews. Results: semantic inconsistency and correlations were identified, portraying the complexity of the components of the Systematization of Nursing Care, with mechanistic fragments. In the conceptual map, three conceptual layers were elaborated. The concepts were organized according to the conceptual proposal of the Systematization of Nursing Care provided for in its main legislation and were later expanded. From this analysis, we proceeded to group by themes: Systematization of Nursing Care; Nursing Actions; Management Care Actions; Nursing Service Management Actions, Care Management Actions; Nursing Service Management Actions; Actions for Application of Care, and Actions for Application in the Nursing Service; Fundamentals, Competencies; Instruments; Regulations and Personnel. From the interviews, 863 record units and six categories emerged: Meaning of Systematization of Nursing Care, with three primary subcategories; Historical Construction of the Concept of Systematization of Nursing Care, with four primary subcategories; Teaching and Learning; Nursing Research; Practical Implications and Implementation of the Systematization of Nursing Care. 156 relevant concepts for ontology modeling were identified using the "101 methodology", aiming to represent the knowledge of the Systematization of Nursing Care domain. Final considerations: the ontology on Systematization of Nursing Care anchored in Complexity Theory allowed a new look at the phenomena, which must be developed, reviewed and re-signified. It is believed that this ontology facilitates the formal representation of knowledge about Systematization of Nursing Care, affirming it as a representative area of knowledge, strengthening its identity, univocal meaning, organization, sharing of knowledge and information. Furthermore, it can favor the diffusion of common vocabulary, contributing to the professional practice of nurses.

Resumen: Introducción: la sistematización de la asistencia de Enfermeríaes la organización del trabajo en cuanto a método, personal e instrumentos, con el objetivo de operacionalizar el proceso de enfermería. Sin embargo, existe una limitación en la comprensión semántica de su significado, conocimiento, operacionalización de sus componentes y el aporte a la práctica y ciencia de enfermería. Objetivo: analizar, en la perspectiva de la Teoría de la Complejidad, el proceso de construcción de un modelo ontológico sobre la sistematización de la asistencia de Enfermería como tecnología de apoyo a la organización de la práctica profesional de enfermería. Método: estudio cualitativo y exploratorio, en tres etapas. En primer lugar, se construyó un mapa conceptual a partir de las siete etapas proclamadas presentadas por Cañas, Novak, Reiska (2015), con el objetivo de identificar conceptos, estructura, procesos y funcionamiento de la sistematización de la asistencia de Enfermería, a la luz de la complejidad, anclada en la literatura relacionada. El conocimiento fue organizado y representado con el apoyo del software CMap Tools. La segunda etapa consistió en entrevistas semiestructuradas, entre mayo y diciembre de 2020, con 17 profesionales, de los cuales nueve del Grupo de Trabajo sobre Sistematización de la Práctica de Enfermería de la Comisión Mixta de la Asociación Brasileña de Enfermería y Consejo Regional de Enfermería-PR y ocho del Comisión Permanente para la Sistematización de la Práctica de Enfermería, designada por el Colegio. Se utilizó el Análisis de Contenido Temático, apoyado en el software MAXQDA. En la tercera etapa, se modeló la representación de una ontología sobre la sistematización de la asistencia de Enfermería, a partir de la guía interactiva Ontology Development 101 con el apoyo del software Protégé (versión 5.5.0), del mapa conceptual y de las entrevistas. Resultados: fueron identificadas inconsistencias semánticas y correlaciones, retratando la complejidad de los componentes de lasistematización de la asistencia de Enfermería, con fragmentos mecanicistas. En el mapa conceptual se elaboraron tres capas conceptuales. Los conceptos fueron organizados de acuerdo con la propuesta conceptual de la sistematización de la asistencia de Enfermeríaprevista en su legislación principal y posteriormente fueron ampliados. A partir de ese análisis, se procedió a agrupar por temas: Sistematización de la Asistencia de Enfermería; Acciones de Enfermería; Acciones de Gestión del Cuidado; Acciones de Gestión del Servicio de Enfermería; Acciones de Gestión del Cuidado; Acciones de Gestión del Servicio de Enfermería; Acciones de Aplicación de Cuidados; Acciones de Aplicación en el Servicio de Enfermería; Fundamentos; Competencias; Instrumentos; Reglamentos y Personal. De las entrevistas surgieron 863 unidades de registro y seis categorías: Significado de sistematización de la asistencia de Enfermería, con tres subcategorías primarias; Construcción Histórica del Concepto de Sistematización de la Atención de Enfermería, con cuatro subcategorías primarias; Enseñanza y Aprendizaje; Investigación en Enfermería; Implicaciones Prácticas e Implementación de la sistematización de la asistencia de Enfermería. Fueron identificados 156 conceptos relevantes para el modelado ontológico utilizando la "metodología 101", con el objetivo de representar el conocimiento del dominio sistematización de la asistencia de Enfermería. Consideraciones finales: la ontología sobre sistematización de la asistencia de Enfermería anclado en la Teoría de la Complejidad permitió una nueva mirada sobre los fenómenos, que deben ser desarrollados, revisados y redefinidos. Se cree que esta ontología facilita la representación formal del conocimiento sobre sistematización de la asistencia de Enfermería, afirmándola como área representativa del saber, fortaleciendo su identidad, sentido unívoco, organización, intercambio de saberes e informaciones. Además, puede favorecer la difusión del vocabulario común, contribuyendo a la práctica profesional de los enfermeros.

Categorizing Sequences of Concern by Function To Better Assess Mechanisms of Microbial Pathogenesis.

To identify sequences with a role in microbial pathogenesis, we assessed the adequacy of their annotation by existing controlled vocabularies and sequence databases. Our goal was to regularize descriptions of microbial pathogenesis for improved integration with bioinformatic applications. Here, we review the challenges of annotating sequences for pathogenic activity. We relate the categorization of more than 2,750 sequences of pathogenic microbes through a controlled vocabulary called Functions of Sequences of Concern (FunSoCs). These allow for an ease of description by both humans and machines. We provide a subset of 220 fully annotated sequences in the supplemental material as examples. The use of this compact (∼30 terms), controlled vocabulary has potential benefits for research in microbial genomics, public health, biosecurity, biosurveillance, and the characterization of new and emerging pathogens.

Equivalence between ICNP® and SNOMED CT concepts: theoretical reflection / Equivalencia entre conceptos ICNP® y SNOMED CT: reflexión teórica / Equivalência entre conceitos da CIPE® e da SNOMED CT: reflexão teórica

ABSTRACT Objective: to reflect on the equivalence between the concepts of the International Classification for Nursing Practice and the Systematized Nomenclature of Medicine International - Clinical Terms. Method: theoretical reflection based on the analysis of equivalence between the concepts of diagnoses, results and nursing interventions of the International Classification for Nursing Practice and the hierarchy of the Systematized Nomenclature of Medicine International - Clinical Terms. The researchers' experience and articles on the subject provided support for analysis. Results: nursing diagnoses and results of the International Classification for Nursing Practice are present in the hierarchies "clinical finding", "disorder" and "problem situation", while the interventions are included in the hierarchies "procedure" and "regime/therapy". The main causes of non-equivalence are linked to the problems of the specificity of the concept. Cross-mapping will require analysis by nursing specialists to improve the representativeness of the concepts. The equivalence table must be translated into Brazilian Portuguese, but the entire Systematized Nomenclature of Medicine International - Clinical Terms lacks interdisciplinary work. Conclusion: the representation of the International Classification for Nursing Practice in systematized Nomenclature of Medicine International - Clinical Terms will bring benefits related to the clarity of concepts. The concepts of nursing classification that are not equivalent will require conceptual analysis. The lack of translation of the Systematized Nomenclature of Medicine International - Clinical Terms for the Portuguese language will reflect the development of terminological subsets of the International Classification for Nursing Practice.

RESUMEN Objetivo: reflexionar sobre la equivalencia entre los conceptos de la Clasificación Internacional para la Práctica de Enfermería y la Nomenclatura Sistematizada de Medicina Internacional - Términos Clínicos. Método: reflexión teórica basada en el análisis de la equivalencia entre los conceptos de diagnósticos, resultados e intervenciones de enfermería de la Clasificación Internacional para la Práctica de Enfermería y la jerarquía de la Nomenclatura Sistematizada de Medicina Internacional - Términos Clínicos. La experiencia de los investigadores y los artículos sobre el tema sirvieron de apoyo para el análisis. Resultados: los diagnósticos y resultados de enfermería de la Clasificación Internacional para la Práctica de Enfermería están presentes en las jerarquías "hallazgo clínico", "trastorno" y "situación-problema", mientras que las intervenciones están incluidas en las jerarquías "procedimiento" y "régimen/terapia". Las principales causas de la no equivalencia están vinculadas a los problemas de especificidad del concepto. El mapeo cruzado requerirá el análisis de expertos en enfermería para mejorar la representatividad de los conceptos. La tabla de equivalencia debe ser traducida al portugués brasileño, pero la totalidad de la Nomenclatura Sistematizada de Medicina Internacional - Términos Clínicos carece de trabajo interdisciplinario. Conclusión: la representación de la Clasificación Internacional para la Práctica de Enfermería en la Nomenclatura Sistematizada de Medicina Internacional - Términos Clínicos traerá beneficios relacionados con la claridad de conceptos. Los conceptos de clasificación de enfermería que no sean equivalentes requerirán un análisis conceptual. La falta de traducción de la Nomenclatura Sistematizada de Medicina Internacional - Términos Clínicos para el portugués se reflejará en el desarrollo de subconjuntos terminológicos de la Clasificación Internacional para la Práctica de Enfermería.

RESUMO Objetivo: refletir sobre a equivalência entre os conceitos da Classificação Internacional para a Prática de Enfermagem e da Systematized Nomenclature of Medicine International - Clinical Terms. Método: reflexão teórica baseada na análise da equivalência entre os conceitos de diagnósticos, resultados e intervenções de enfermagem da Classificação Internacional para a Prática de Enfermagem e a hierarquia da Systematized Nomenclature of Medicine International - Clinical Terms. A experiência das pesquisadoras e artigos sobre o tema ofereceram suporte para análise. Resultados: diagnósticos e resultados de enfermagem da Classificação Internacional para a Prática de Enfermagem estão presentes nas hierarquias "achado clínico", "transtorno" e "situação-problema", enquanto as intervenções constam nas hierarquias "procedimento" e "regime/terapia". As principais causas de não equivalência são ligadas aos problemas da especificidade do conceito. O mapeamento cruzado exigirá análise por especialistas na enfermagem para melhorar a representatividade dos conceitos. A tabela de equivalência deverá ser traduzida para o português brasileiro, porém a totalidade da Systematized Nomenclature of Medicine International - Clinical Terms carece de trabalho interdisciplinar. Conclusão: a representação da Classificação Internacional para a Prática de Enfermagem na Systematized Nomenclature of Medicine International - Clinical Terms trará benefícios relacionados à clareza dos conceitos. Os conceitos da classificação de enfermagem que não foram equivalentes necessitarão de análise conceitual. A ausência de tradução da Systematized Nomenclature of Medicine International - Clinical Terms para o português refletirá no desenvolvimento de subconjuntos terminológicos da Classificação Internacional para a Prática de Enfermagem.

Biomedical Ontologies to Guide AI Development in Radiology.

The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain's terms through their relationships with other terms in the ontology. Those relationships, then, define the terms' semantics, or "meaning." Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machine-computable. Some ontologies, such as RSNA's RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image-based machine learning, radiomics, and planning.

O-JMeSH: creating a bilingual English-Japanese controlled vocabulary of MeSH UIDs through machine translation and mutual information.

Previous approaches to create a controlled vocabulary for Japanese have resorted to existing bilingual dictionary and transformation rules to allow such mappings. However, given the possible new terms introduced due to coronavirus disease 2019 (COVID-19) and the emphasis on respiratory and infection-related terms, coverage might not be guaranteed. We propose creating a Japanese bilingual controlled vocabulary based on MeSH terms assigned to COVID-19 related publications in this work. For such, we resorted to manual curation of several bilingual dictionaries and a computational approach based on machine translation of sentences containing such terms and the ranking of possible translations for the individual terms by mutual information. Our results show that we achieved nearly 99% occurrence coverage in LitCovid, while our computational approach presented average accuracy of 63.33% for all terms, and 84.51% for drugs and chemicals.

Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language.

Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.