A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application

BACKGROUND

The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively.

MATERIALS AND METHODS

The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers.

RESULTS

WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness.

CONCLUSIONS

Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Ontological approach for competency-based curriculum analysis

This article is dedicated to the development of a model for competencies within an educational program and its implementation through the use of semantic technologies. The model proposed by the authors is distinctive in that competencies are organized into a hierarchical data structure with arbitrary levels of nesting. Furthermore, the article presents an original solution for modelling the input requirements for studying a course, which is defined in the form of dependencies between the competencies generated by the course and the competencies of other courses. The outcome of this work is an ontological model of a competency-based curriculum, for which the authors have developed and implemented algorithms for data addition and retrieval, as well as for analyzing the consistency of the curriculum in terms of the input requirements for studying a discipline and the learning outcomes from previous periods. The findings presented in the article will prove to be valuable in the development of educational process management information systems and educational program constructors. They will also be instrumental in aligning diverse educational programs within the context of academic mobility.

Towards a social harm approach in drug policy

In this paper, we explore how the social harm approach can be adapted within drug policy scholarship. Since the mid-2000s, a group of critical criminologists have moved beyond the concept of crime and criminology, towards the study of social harm. This turn proceeds decades of research that highlights the inequities within the criminal legal system, the formation of laws that protect the privileged and punish the disadvantaged, and the systemic challenge of the effectiveness of retribution and punishment at addressing harm in the community. The purpose of this paper is to first identify parallels between the social harm approach and critical drug scholarship, and second to advocate for the adoption of a social harm lens in drug policy scholarship. In the paper, we draw out the similarities between social harm and drug policy literatures, as well as outline what the study of social harm can bring to an analysis of drug policy. This includes a discussion on the ontology of drug crime, the myth of drug crime and the ineffective use of the crime control system in response to drug use. The paper then discusses how these conversations in critical criminology and critical drugs scholarship can be brought together to inform future drug policy research. This reflection details the link between social harm and the impingement of human flourishing, explores the role of decolonizing drug policy, advocates for the centralization of lived experience within the research process and outlines how this might align with harm reduction approaches. We conclude by arguing that the social harm approach challenges the idea that neutrality is the goal in drug policy and explicitly seeks to expand new avenues in activist research and social justice approaches to policymaking.

Ontological pluralism and social values

There seems to be an emerging consensus among many philosophers of science that non-epistemic values ought to play a role in the process of scientific reasoning itself. Recently, a number of philosophers have focused on the role of values in scientific classification or taxonomy. Their claim is that a choice of ontology or taxonomic scheme can only be made, or should only be made, by appealing to non-epistemic or social values. In this paper, I take on this "argument from ontological choice," claiming that it equivocates on the notion of choice. An ontological choice can be understood either in terms of determining which taxonomic scheme is valid, or in terms of deciding which taxonomic scheme to deploy in a given context. I try to show that while the latter can be determined in part by social values, the former ought not to be so determined.

Comparative analysis of peripheral whole blood transcriptome from asymptomatic carriers reveals upregulation of subsets of surface proteins implicated in Plasmodium falciparum phenotypic plasticity

The molecular mechanism underlying Plasmodium falciparum's persistence in the asymptomatic phase of infection remains largely unknown. However, large-scale shifts in the parasites' gene expression during asymptomatic infections may enhance phenotypic plasticity, maximizing their fitness and leading to the persistence of the asymptomatic infections. To uncover these mechanisms, we aimed to identify parasite genetic factors implicated in asymptomatic infections through whole transcriptome analysis. We analyzed publicly available transcriptome datasets containing asymptomatic malaria (ASM), uncomplicated malaria (SM), and malaria-naïve (NSM) samples from 35 subjects for differentially expressed genes (DEGs) and long noncoding RNAs. Our analysis identified 755 and 1773 DEGs in ASM vs SM and NSM, respectively. These DEGs revealed sets of genes coding for proteins of unknown functions (PUFs) upregulated in ASM vs SM and ASM, suggesting their role in underlying fundamental molecular mechanisms during asymptomatic infections. Upregulated genes in ASM vs SM revealed a subset of 24 clonal variant genes (CVGs) involved in host-parasite and symbiotic interactions and modulation of the symbiont of host erythrocyte aggregation pathways. Moreover, we identified 237 differentially expressed noncoding RNAs in ASM vs SM, of which 11 were found to interact with CVGs, suggesting their possible role in regulating the expression of CVGs. Our results suggest that P. falciparum utilizes phenotypic plasticity as an adaptive mechanism during asymptomatic infections by upregulating clonal variant genes, with long noncoding RNAs possibly playing a crucial role in their regulation. Thus, our study provides insights into the parasites' genetic factors that confer a fitness advantage during asymptomatic infections.

Life in Suspension with Death: Biocultural Ontologies, Perceptual Cues, and Biomarkers for the Tibetan Tukdam Postmortem Meditative State

This article presents two cases from a collaborative study among Tibetan monastic populations in India on the postdeath meditative state called tukdam (thugs dam). Entered by advanced Tibetan Buddhist practitioners through a variety of different practices, this state provides an ontological frame that is investigated by two distinct intellectual traditions-the Tibetan Buddhist and medical tradition on one hand and the Euroamerican biomedical and scientific tradition on the other-using their respective means of inquiry. Through the investigation, the traditions enact two paradigms of the body at the time of death alongside attendant conceptualizations of what constitutes life itself. This work examines when epistemologies of these two traditions might converge, under what ontological contexts, and through which correlated indicators of evidence. In doing so, this work explores how these two intellectual traditions might answer how the time course and characteristics of physiological changes during the postmortem period might exhibit variation across individuals. Centrally, this piece presents an epistemological inquiry delineating the types of valid evidence that constitute exceptional processes post-clinical death and their potential ontological implications.

New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

BACKGROUND

Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield.

RESULTS

Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield.

CONCLUSIONS

Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms

PURPOSE

The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework.

METHODS

Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated.

RESULTS

We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources.

CONCLUSION

The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Intelligent search system for resume and labor law

Labor and employment are important issues in social life. The demand for online job searching and searching for labor regulations in legal documents, particularly regarding the policy for unemployment benefits, is essential. Nowadays, each function has some programs for its working. However, there is no program that combines both functions. In practice, when users seek a job, they may be unemployed or want to transfer to another work. Thus, they are required to search for regulations about unemployment insurance policies and related information, as well as regulations about workers working smoothly and following labor law. Ontology is a useful technique for representing areas of practical knowledge. This article proposes an ontology-based method for solving labor and employment-related problems. First, we construct an ontology of job skills to match curriculum vitae (CV) and job descriptions (JD). In addition, an ontology for representing labor law documents is proposed to aid users in their search for legal labor law regulations. These ontologies are combined to construct the knowledge base of a job-searching and labor law-searching system. In addition, this integrated ontology is used to study several issues involving the matching of CVs and JDs and the search for labor law issues. A system for intelligent resume searching in information technology is developed using the proposed method. This system also incorporates queries pertaining to Vietnamese labor law policies regarding unemployment and healthcare benefits. The experimental results demonstrate that the method designed to assist job seekers and users searching for legal labor documents is effective.

The Assemblage and Dismantling of Access Barriers in Administrative Bureaucracies: Constructing the Problem of Diversity in the German Welfare State

The article extends the literature on the construction of "diversity management" by personnel managers in corporate America. Such research has highlighted that Human Resource (HR) specialists draw heavily on social-scientific thinking in implementing various remedies against discrimination. However, it has paid less attention to how such esoteric views of reality, comprising such "things" as "structural barriers" impeding occupational advancement and "diversity sensitivity," have been successfully established as a self-evident reality in the workplace. In order to more thoroughly investigate how the world of diversity management is established outside the circle of academic specialists, the article employs perspectives from science and technology studies on the ways in which sociotechnical assemblages, i.e., networks of human actors and material devices, enact scientific ontologies. It applies such perspectives to a German case of diversity management, a program of "intercultural opening" that seeks to make bureaucracies of the welfare state more accessible to immigrants. The article delineates the specific ontology behind this version of diversity management, rooted in sociological perspectives on social mobility, and explores the various techniques and instruments through which officers of intercultural opening establish this ontology as a visible reality in municipal administrations.

Obtaining clinical term embeddings from SNOMED CT ontology

Clinical term embeddings are traditionally obtained using corpus-based methods, however, these methods cannot incorporate knowledge about clinical terms which is already present in medical ontologies. On the other hand, graph-based methods can obtain embeddings of clinical concepts from ontologies, but they cannot obtain embeddings for clinical terms and words. In this paper, a novel method is presented to obtain embeddings for clinical terms and words from the SNOMED CT ontology. The method first obtains embeddings of clinical concepts from SNOMED CT using a graph-based method. Next, these concept embeddings are used as targets to train a deep learning model to map clinical terms to concepts embeddings. The learned model then provides embeddings for clinical terms and words as well as maps novel clinical terms to their embeddings. The embeddings obtained using the method out-performed corpus-based embeddings on the task of predicting clinical term similarity on five benchmark datasets. On the clinical term normalization task, using these embeddings simply as a means of computing similarity between clinical terms obtained accuracy which was competitive to methods trained specifically for this task. Both corpus-based and ontology-based embeddings have a limitation that they tend to learn similar embeddings for opposite or analogous terms. To counter this, we also introduce a method to automatically learn patterns that indicate when two clinical terms represent the same concept and when they represent different concepts. Supplementing the normalization process with these patterns showed improvement. Although clinical term embeddings obtained from SNOMED CT incorporate ontological knowledge which is missed by corpus-based embeddings, they do not incorporate linguistic knowledge which is needed for sentence-based tasks. Hence combining ontology-based embeddings with corpus-based embeddings is an avenue for future work.

HeNeCOn: An ontology for integrative research in Head and Neck cancer

BACKGROUND

Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, which motivates the construction of a high-quality harmonization model to standardize this multi-source clinical data in terms of format and semantics. The use of ontologies and semantic techniques is a well-known approach to face this challenge.

OBJECTIVE

This work aims to provide a clinically reliable data model for HNC processes during all phases of the disease: prognosis, treatment, and follow-up. Therefore, we built the first ontology specifically focused on the HNC domain, named HeNeCOn (Head and Neck Cancer Ontology).

METHODS

First, an annotated dataset was established to provide a formal reference description of HNC. Then, 170 clinical variables were organized into a taxonomy, and later expanded and mapped to formalize and integrate multiple databases into the HeNeCOn ontology. The outcomes of this iterative process were reviewed and validated by clinicians and statisticians.

RESULTS

HeNeCOn is an ontology consisting of 502 classes, a taxonomy with a hierarchical structure, semantic definitions of 283 medical terms and detailed relations between them, which can be used as a tool for information extraction and knowledge management.

CONCLUSION

HeNeCOn is a reusable, extendible and standardized ontology which establishes a reference data model for terminology structure and standard definitions in the Head and Neck Cancer domain. This ontology allows handling both current and newly generated knowledge in Head and Neck cancer research, by means of data linking and mapping with other public ontologies.

The human-centric Industry 5.0 collaboration architecture

While the primary focus of Industry 4.0 revolves around extensive digitalization, Industry 5.0, on the other hand, seeks to integrate innovative technologies with human actors, signifying an approach that is more value-driven than technology-centric. The key objectives of the Industry 5.0 paradigm, which were not central to Industry 4.0, underscore that production should not only be digitalized but also resilient, sustainable, and human-centric. This paper is focusing on the human-centric pillar of Industry 5.0. The proposed methodology addresses the need for a human-AI collaborative process design and innovation approach to support the development and deployment of advanced AI-driven co-creation and collaboration tools. The method aims to solve the problem of integrating various innovative agents (human, AI, IoT, robot) in a plant-level collaboration process through a generic semantic definition, utilizing a time event-driven process. It also encourages the development of AI techniques for human-in-the-loop optimization, incorporating cross-checking with alternative feedback loop models. Benefits of this methodology include the Industry 5.0 collaboration architecture (I5arc), which provides new adaptable, generic frameworks, concepts, and methodologies for modern knowledge creation and sharing to enhance plant collaboration processes. •The I5arc aims to investigate and establish a truly integrated human-AI collaboration model, equipped with methods and tools for human-AI driven co-creation.•Provide a framework for the co-execution of processes and activities, with humans remaining empowered and in control.•The framework primarily targets human-AI collaboration processes and activities in industrial plants, with potential applicability to other societal contexts.

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage

BACKGROUND

Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies.

METHODS

We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems.

RESULTS

Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies.

CONCLUSION

Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.

Construction of injury process from Japanese consumer product narrative injury data using an ontology-based method

Understanding of how injuries occur plays an effective role in accident learning and prevention. Existing frameworks focus on crucial information but ignore their causal relationships, which can lead to an incomplete understanding of the injury process. In this study, the descriptive framework of injury data (DFID) is expanded and combined with accident causation models used to elaborate on the causality of each injury factor. Subsequently, the injury process description ontology (IPD-Onto) based on DFID (extension) is established through a seven-step method developed by Stanford University. The IPD-Onto divides injury cases into five unified classes and constructs the injury process through the object properties. The ontology-based description of the injury process (with causal relationships) provides additional description and interpretation capabilities that are understandable by human experts or computers. The results of the Protégé DL query show that the ontology-based method enables the machine to interpret the injury process.

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach

BACKGROUND

Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval.

METHODS

This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation.

RESULTS

We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching.

CONCLUSION

The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

Strategy maintenance in smart healthcare systems

BACKGROUNDS

The size of medical strategies is expected to grow in conjunction with the expansion of modern diseases' complexity. When a strategy includes more than ten statements, its manual management becomes very challenging, and in some cases, impossible. As a result, the updates they get may result in the unavoidable appearance of anomalies. This causes an interruption in the outflow of imperfect knowledge.

METHODS

In this paper, we propose an approach called TAnom-HS to verify healthcare strategies. We focus on the management and maintenance, in a convenient and automatic way, of a large strategy to guarantee knowledge accuracy and enhance the efficiency of the inference process in healthcare systems.

RESULTS

We developed a prototype of our proposal and we applied it on some cases from the BioPortal repository. The evaluation of both steps of TAnom-HS proved the efficiency of our proposal.

CONCLUSION

To increase ontologies expressiveness, a set of rules called strategy is added to it. TAnom-HS is a two-step approach that treats anomalies in healthcare strategies. Such a task helps to take automatic and efficient healthcare decisions.

Five urban health research traditions: A meta-narrative review

Urban health scholars explore the connection between the urban space and health through ontological perspectives that are shaped by their disciplinary traditions. Without explicit recognition of the different approaches, there are barriers to collaboration. This paper maps the terrain of the urban health scholarship to identify key urban health research traditions; and to articulate the main features distinguishing these different traditions. We apply a meta-narrative review guided by a bibliometric co-citation network analysis to the body of research on urban health retrieved from the Web of Science Core Collection. Five urban health research traditions were identified: (1) sustainable urban development, (2) urban ecosystem services, (3) urban resilience, (4) healthy urban planning, and (5) urban green spaces. Each research tradition has a different conceptual and thematic perspective to addressing urban health. These include perspectives on the scale of the urban health issue of interest, and on the conceptualisation of the urban context and health. Additionally, we developed a framework to allow for better differentiation between the differing research traditions based on (1) perspectives of the urban system as complicated or complex, (2) the preferred locus of change as a function of structure and agency and (3) the geographic scale of the urban health issue that is addressed. These dimensions have even deeper implications for transdisciplinary collaboration as they are underpinned by paradigmatic differences, rather than disciplinary differences. We conclude that it is essential for urban health researchers to reflect on the different urban health approaches and seek coherence by understanding their similarities and differences. Such endeavours are required to produce and interpret transdisciplinary knowledge for the goal of improving health by transforming urban systems.

Positive youth development as a guiding framework in sport research: Is it time to plan for a transition?

Positive youth development is a popular guiding framework for studying the psychosocial development of youth. In sport research, for more than two decades, this framework has enhanced our understanding of the mechanisms involved in successful shifts from youth to adulthood. Nonetheless, scholars have recently taken a more critical stance on the positive youth development framework by elucidating some of its shortcomings. To help determine whether it may be warranted to plan for a transition from the positive youth development framework in sport research, a critical commentary is offered. The purpose of this commentary lies in situating three ontologically distinct arguments that depict the shortcomings of the positive youth development framework, namely the operationalization argument, the social justice argument, and the posthumanist argument. This paper is offered as an open invitation to instigate dialogue on what may come next for youth development in sport research and whether planning for a transition is warranted.

Augmented intelligence facilitates concept mapping across different electronic health records

INTRODUCTION

With the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports.

METHODS

We used the manually mapped parameter names from the multicenter "Dutch ICU data warehouse against COVID-19" sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons.

RESULTS

The initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters.

CONCLUSION

Augmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential.

A revised terminology for male genitalia in Hymenoptera (Insecta), with a special emphasis on Ichneumonoidea

Applying consistent terminology for morphological traits across different taxa is a highly pertinent task in the study of morphology and evolution. Different terminologies for the same traits can generate bias in phylogeny and prevent correct homology assessments. This situation is exacerbated in the male genitalia of Hymenoptera, and specifically in Ichneumonoidea, in which the terminology is not standardized and has not been fully aligned with the rest of Hymenoptera. In the current contribution, we review the terms used to describe the skeletal features of the male genitalia in Hymenoptera, and provide a list of authors associated with previously used terminology. We propose a unified terminology for the male genitalia that can be utilized across the order and a list of recommended terms. Further, we review and discuss the genital musculature for the superfamily Ichneumonoidea based on previous literature and novel observations and align the terms used for muscles across the literature.

Moving beyond a figurative psychedelic literacy: Metaphors of psychiatric symptoms in ayahuasca narratives

Metaphors, analogies, and similes commonly appear in narratives of drinking the potent psychedelic "ayahuasca", presenting an intriguing transcultural pattern. Based upon survey and field research at an ayahuasca healing center in Pucallpa, Peru, the article investigates conceptual metaphors in narratives of ayahuasca experiences made by the visiting international guests. Bodily metaphors and visionary analogies frequently appear in narrative plots where they can express the reappraisal, overcoming, and sometimes emboldening of symptoms diagnosed by psychiatry. Moving beyond the literal-figurative divide, the article explores the intrinsic "metaphoricity" of psychedelic experiences and advocates for a literacy of conceptual metaphors regarding both clinical and non-clinical psychedelic narratives. Developing this literacy can broaden approaches in psychedelic psychiatry that analyze and treat syndromes and disorders, while also being applicable to social science and humanities research that examine psychoactive drug use beyond medical frameworks.

From free-text electronic health records to structured cohorts: Onconum, an innovative methodology for real-world data mining in breast cancer

PURPOSE

A considerable amount of valuable information is present in electronic health records (EHRs) however it remains inaccessible because it is embedded into unstructured narrative documents that cannot be easily analyzed. We wanted to develop and evaluate a methodology able to extract and structure information from electronic health records in breast cancer.

METHODS

We developed a software platform called Onconum (ClinicalTrials.gov Identifier: NCT02810093) which uses a hybrid method relying on machine learning approaches and rule-based lexical methods. It is based on natural language processing techniques that allows a targeted analysis of free-text medical data related to breast cancer, independently of any pre-existing dictionary, in a French context (available in N files). We then evaluated it on a validation cohort called Senometry.

FINDINGS

Senometry cohort included 9,599 patients with breast cancer (both invasive and in situ), treated between 2000 and 2017 in the breast cancer unit of Strasbourg University Hospitals. Extraction rates ranged from 45 to 100%, depending on the type of each parameter. Precision of extracted information was 68%-94% compared to a structured cohort, and 89%-98% compared to manually structured databases and it retrieved more rare occurrences compared to another database search engine (+17%).

INTERPRETATION

This innovative method can accurately structure relevant medical information embedded in EHRs in the context of breast cancer. Missing data handling is the main limitation of this method however multiple sources can be incorporated to reduce this limit. Nevertheless, this methodology does not need neither pre-existing dictionaries nor manually annotated corpora. It can therefore be easily implemented in non-English-speaking countries and in other diseases outside breast cancer, and it allows prospective inclusion of new patients.

An approach for proteins and their encoding genes synonyms integration based on protein ontology

BACKGROUND

Biological research is generating high volumes of data distributed across various sources. The inconsistent naming of proteins and their encoding genes brings great challenges to protein data integration: proteins and their coding genes usually have multiple related names and notations, which are difficult to match absolutely; the nomenclature of genes and proteins is complex and varies from species to species; some less studied species have no nomenclature of genes and proteins; The annotation of the same protein/gene varies greatly in different databases. In summary, a comprehensive set of protein/gene synonyms is necessary for relevant studies.

RESULTS

In this study, we propose an approach for protein and its encoding gene synonym integration based on protein ontology. The workflow of protein and gene synonym integration is composed of three modules: data acquisition, entity and attribute alignment, attribute integration and deduplication. Finally, the integrated synonym set of proteins and their coding genes contains over 128.59 million terminologies covering 560,275 proteins/genes and 13,781 species. As the semantic basis, the comprehensive synonym set was used to develop a data platform to provide one-stop data retrieval without considering the diversity of protein nomenclature and species.

CONCLUSION

The synonym set constructed here can serve as an important resource for biological named entity identification, text mining and information retrieval without name ambiguity, especially synonyms associated with well-defined species categories can help to study the evolutionary relationships between species at the molecular level. More importantly, the comprehensive synonyms set is the semantic basis for our subsequent studies on Protein-protein Interaction (PPI) knowledge graph.

"The Dynamic Nature of Being a Person": An Ethnographic Study of People Living With Dementia in Their Communities

BACKGROUND AND OBJECTIVES

A dementia diagnosis can affect social interactions. This study aims to understand how people living with dementia act as social beings within everyday interactions in their local communities.

RESEARCH DESIGN AND METHODS

Focused ethnography informed by Spradley's approach to data collection and analysis. Observations in community spaces.

RESULTS

Twenty-nine observations were undertaken in everyday social settings with 11 people with dementia who were part of a longitudinal interview study. Data consisted of 40 hr of observation, and researcher field notes. The overarching theme "the dynamic nature of being a person" encapsulates participants' exhibited experiences in negotiating to attain and sustain an acknowledged place in their communities. Two subthemes characterized contexts and actions: (1) "Being me-not dementia": Participants constructed narratives to assert their ontological presence in social settings. They and others used strategies to mediate cognitive changes evidencing dementia. (2) "Resisting or acquiescing to 'being absent in place'": Participants were often able to resist being absent to the gaze from others, but some social structures and behaviors led to a person being "in place," yet not having their presence confirmed.

DISCUSSION AND IMPLICATIONS

People living with dementia can actively draw on personal attributes, familiar rituals, objects, and social roles to continue to present themselves as social beings. Identifying how postdiagnosis people may self-manage cognitive changes to retain their presence as a person can help health and social care practitioners and families collaborate with the person living with dementia enabling them to have a continued social presence.

Geometric feature extraction in manufacturing based on a knowledge graph

In times of global crises, the resilience of production chains is becoming increasingly important. If a supply chain is interrupted, a cost-effective solution must be established quickly. In the context of Industry 4.0, the concept of smart manufacturing offers a solution for fast and automated decision-making in production planning. The core idea of smart manufacturing is the digitalization of the product life cycle and the linking of individual phases of this cycle. Computer Aided Process Planning (CAPP) plays an important role as the connecting element between design and manufacturing. An important prerequisite for CAPP is the automated analysis of 3D models of components. The aim of this work is the development of an automatic feature recognition (AFR) -method to recognize geometric manufacturing features and their properties from 3D-models and then store them in a knowledge base. In that way, the result of the design can be automatically analysed and compared with manufacturing information afterwards in order to achieve an automated process planning. Geometric and topological information of a 3D model (STEP-AP242 format) generated by CAD systems is extracted by a Python-script developed and stored in an ontology-based knowledge base. The extracted product data is analysed using a Python-script to identify manufacturing features. To provide a comprehensive extensibility of the model, geometric features are defined according to a layered and hierarchical structure.

Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions

The environmental damage caused by air pollution has recently become the focus of city council policies. The concept of the green city has emerged as an urban solution by which to confront environmental challenges worldwide and is founded on air pollution levels that have increased meaningfully as a result of traffic in urban areas. Local governments are attempting to meet environmental challenges by developing public traffic policies such as air pollution protocols. However, several problems must still be solved, such as the need to link smart cars to these pollution protocols in order to find more optimal routes. We have, therefore, attempted to address this problem by conducting a study of local policies in the city of Madrid (Spain) with the aim of determining the importance of the vehicle routing problem (VRP), and the need to optimise a set of routes for a fleet. The results of this study have allowed us to propose a framework with which to dynamically implement traffic constraints. This framework consists of three main layers: the data layer, the prediction layer and the event generation layer. With regard to the data layer, a dataset has been generated from traffic data concerning the city of Madrid, and deep learning techniques have then been applied to this data. The results obtained show that there are interdependencies between several factors, such as weather conditions, air quality and the local event calendar, which have an impact on drivers' behaviour. These interdependencies have allowed the development of an ontological model, together with an event generation system that can anticipate changes and dynamically restructure traffic restrictions in order to obtain a more efficient traffic system. This system has been validated using real data from the city of Madrid.

Modelling digital health data: The ExaMode ontology for computational pathology

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices.

Material and methods

This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology.

Results

The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data.

Discussion

The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries.

DEVO: an ontology to assist with dermoscopic feature standardization

BACKGROUND

The utilization of dermoscopic analysis is becoming increasingly critical for diagnosing skin diseases by physicians and even artificial intelligence. With the expansion of dermoscopy, its vocabulary has proliferated, but the rapid evolution of the vocabulary of dermoscopy without standardized control is counterproductive. We aimed to develop a domain-specific ontology to formally represent knowledge for certain dermoscopic features.

METHODS

The first phase involved creating a fundamental-level ontology that covers the fundamental aspects and elements in describing visualizations, such as shapes and colors. The second phase involved creating a domain ontology that harnesses the fundamental-level ontology to formalize the definitions of dermoscopic metaphorical terms.

RESULTS

The Dermoscopy Elements of Visuals Ontology (DEVO) contains 1047 classes, 47 object properties, and 16 data properties. It has a better semiotic score compared to similar ontologies of the same domain. Three human annotators also examined the consistency, complexity, and future application of the ontology.

CONCLUSIONS

The proposed ontology was able to harness the definitions of metaphoric terms by decomposing them into their visual elements. Future applications include providing education for trainees and diagnostic support for dermatologists, with the goal of generating responses to queries about dermoscopic features and integrating these features to diagnose skin diseases.

Determining the utility of diagnostic genomics: a conceptual framework

BACKGROUND

Diagnostic efficacy is now well established for diagnostic genomic testing in rare disease. Assessment of overall utility is emerging as a key next step, however ambiguity in the conceptualisation and measurement of utility has impeded its assessment in a comprehensive manner. We propose a conceptual framework to approach determining the broader utility of diagnostic genomics encompassing patients, families, clinicians, health services and health systems to assist future evidence generation and funding decisions. BODY: Building upon previous work, our framework posits that utility of diagnostic genomics consists of three dimensions: the domain or type and extent of utility (what), the relationship and perspective of utility (who), and the time horizon of utility (when). Across the description, assessment, and summation of these three proposed dimensions of utility, one could potentially triangulate a singular point of utility axes of type, relationship, and time. Collectively, the multiple different points of individual utility might be inferred to relate to a concept of aggregate utility.

CONCLUSION

This ontological framework requires retrospective and prospective application to enable refinement and validation. Moving forward our framework, and others which have preceded it, promote a better characterisation and description of genomic utility to inform decision-making and optimise the benefits of genomic diagnostic testing.

An ontology-based approach for modelling and querying Alzheimer's disease data

BACKGROUND

The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.

METHODS

Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.

RESULTS

We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.

CONCLUSIONS

The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources

BACKGROUND

In the United States, the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) are two major data sharing resources for Alzheimer's Disease (AD) research. NACC and ADNI strive to make their data more FAIR (findable, interoperable, accessible and reusable) for the broader research community. However, there is limited work harmonizing and supporting cross-cohort interoperability of the two resources.

METHOD

In this paper, we leverage an ontology-based approach to harmonize data elements in the two resources and develop a web-based query system to search patient cohorts across the two resources. We first mapped data elements across NACC and ADNI, and performed value harmonization for the mapped data elements with inconsistent permissible values. Then we built an Alzheimer's Disease Data Element Ontology (ADEO) to model the mapped data elements in NACC and ADNI. We further developed a prototype cross-cohort query system to search patient cohorts across NACC and ADNI.

RESULTS

After manual review, we found 172 mappings between NACC and ADNI. These 172 mappings were further used to construct common concepts in ADEO. Our data element mapping and harmonization resulted in five files storing common concepts, variables in NACC and ADNI, mappings between variables and common concepts, permissible values of categorical type data elements, and coding inconsistency harmonization, respectively. Our cross-cohort query system consists of three core architectural elements: a web-based interface, an advanced query engine, and a backend MongoDB database.

CONCLUSIONS

In this work, ADEO has been specifically designed to facilitate data harmonization and cross-cohort query of NACC and ADNI data resources. Although our prototype cross-cohort query system was developed for exploring NACC and ADNI, its backend and frontend framework has been designed and implemented to be generally applicable to other domains for querying patient cohorts from multiple heterogeneous data sources.

Determining the dominant understandings in security science literature in Turkey: A bibliometric analysis on the journal of security science

The development of a scientific discipline is closely related to the quantity and quality of research conducted within that discipline. Therefore, examining the studies published in a particular field is crucial for evaluating and understanding its development. While there are numerous national and international studies that assess the quality and quantity of research in various disciplines, no such study has been found specifically for the field of security science. This study aims to determine the subjects, approaches, and methods used in research within the field of security science. By analyzing 129 articles published in the Journal of Security Sciences between 2012 and 2021, the study seeks to provide insights and recommendations for the advancement of research in this field. The articles were subjected to bibliometric analysis, considering various criteria such as research objectives, methods, topics, and theoretical foundations. The contributions of these studies to the development of the discipline were evaluated. Based on the analysis of these articles, it is evident that methodological aspects of studies in this field are still in the early stages. The most significant deficiency identified is the lack of practical research in the analyzed articles. Conducting empirical studies is deemed essential for the advancement of security science. Consequently, several suggestions are proposed for future research. Moreover, an evaluation is provided as a foundation for future studies in the field of security science.

Features of a FAIR vocabulary

BACKGROUND

The Findable, Accessible, Interoperable and Reusable(FAIR) Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able to define FAIR vocabularies, identify features of FAIR vocabularies, and provide assessment approaches against the features can guide the development of vocabularies.

RESULTS

We differentiate data, data resources and vocabularies used for FAIR, examine the application of the FAIR Principles to vocabularies, align their requirements with the Open Biomedical Ontologies principles, and propose FAIR Vocabulary Features. We also design assessment approaches for FAIR vocabularies by mapping the FVFs with existing FAIR assessment indicators. Finally, we demonstrate how they can be used for evaluating and improving vocabularies using exemplary biomedical vocabularies.

CONCLUSIONS

Our work proposes features of FAIR vocabularies and corresponding indicators for assessing the FAIR levels of different types of vocabularies, identifies use cases for vocabulary engineers, and guides the evolution of vocabularies.

A non-clinical and clinical IUCLID database for 530 pharmaceuticals (part I): Methodological aspects of its development

A new IUCLID database is provided containing results from non-clinical animal studies and human information for 530 approved drugs. The database was developed by extracting data from pharmacological reviews of repeat-dose, carcinogenicity, developmental, and reproductive toxicity studies. In the database, observed and no-observed effects are linked to the respective effect levels, including information on severity/incidence and transiency/reversibility. It also includes some information on effects in humans, that were extracted from relevant sections of standard product labels of the approved drugs. The database is complemented with a specific ontology for reporting effects that was developed as an improved version of the Ontology Lookup Service's mammalian and human phenotype ontologies and includes different hierarchical levels. The developed ontology contains novel and unique standardized terms, including ontological terms for reproductive and endocrine effects. The new IUCLID database aims to facilitate correlation and concordance analyses based on the link between observed and no-observed effects and their respective effect levels. In addition, it offers a robust dataset on drug information for the pharmaceutical industry and research. The reported ontology supports the analyses of toxicological information, especially for reproductive and endocrine endpoints and can be used to encode legacy data or develop additional ontologies. The new database and ontology can be used to support the development of alternative non-animal approaches, to elucidate mechanisms of toxicity, and to analyse human relevance. The IUCLID database is provided free of charge at https://iuclid6.echa.europa.eu/us-fda-toxicity-data.

RAMPVIS: A visualization and visual analytics infrastructure for COVID-19 data

The COVID-19 pandemic generated large amounts of diverse data, including testing, treatments, vaccine trials, data from modeling, etc. To support epidemiologists and modeling scientists in their efforts to understand and respond to the pandemic, there arose a need for web visualization and visual analytics (VIS) applications to provide insights and support decision-making. In this paper, we present RAMPVIS, an infrastructure designed to support a range of observational, analytical, model-developmental, and dissemination tasks. One of the main features of the system is the ability to "propagate" a visualization designed for one data source to similar ones, this allows a user to quickly visualize large amounts of data. In addition to the COVID pandemic, the RAMPVIS software may be adapted and used with different data to provide rapid visualization support for other emergency responses.

Ontology-Based Semantic Annotation of French Psychiatric Clinical Documents

Building a timeline of psychiatric patient profiles can answer many valuable questions, such as how important medical events affect the progression of psychosis in patients. However, the majority of text information extraction and semantic annotation tools, as well as domain ontologies, are only available in English and cannot be easily extended to other languages, due to fundamental linguistic differences. In this paper, we describe a semantic annotation system based on an ontology developed in the PsyCARE framework. Our system is being manually evaluated by two annotators on 50 patient discharge summaries, showing promising results.

Improving Antibiotic Prescribing for Dentistry in France Using an Ontology

Antibiotic overprescribing in dentistry is a major concern that contributes to the emergence of antimicrobial resistance. It is due in part to the misuse of antibiotics by dentists but also by other practitioners who see patients in emergency for dental care. We used the Protégé software to create an ontology regarding the most common dental diseases and the most used antibiotics to treat them. It is an easy shareable knowledge base that could be used directly as decision support tool to improve the use of antibiotics in dental care.

A Categorial Structure for Identifying Physiological Measurement Observables

Patient measurements to characterize pathophysiological phenomena are often difficult to describe. A controlled terminology for this domain is needed, to describe methods, appropriate reference values and to report results. This is a proposal for a categorial structure for such a terminology.

A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video

BACKGROUND

Annotated data are foundational to applications of supervised machine learning. However, there seems to be a lack of common language used in the field of surgical data science. The aim of this study is to review the process of annotation and semantics used in the creation of SPM for minimally invasive surgery videos.

METHODS

For this systematic review, we reviewed articles indexed in the MEDLINE database from January 2000 until March 2022. We selected articles using surgical video annotations to describe a surgical process model in the field of minimally invasive surgery. We excluded studies focusing on instrument detection or recognition of anatomical areas only. The risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data from the studies were visually presented in table using the SPIDER tool.

RESULTS

Of the 2806 articles identified, 34 were selected for review. Twenty-two were in the field of digestive surgery, six in ophthalmologic surgery only, one in neurosurgery, three in gynecologic surgery, and two in mixed fields. Thirty-one studies (88.2%) were dedicated to phase, step, or action recognition and mainly relied on a very simple formalization (29, 85.2%). Clinical information in the datasets was lacking for studies using available public datasets. The process of annotation for surgical process model was lacking and poorly described, and description of the surgical procedures was highly variable between studies.

CONCLUSION

Surgical video annotation lacks a rigorous and reproducible framework. This leads to difficulties in sharing videos between institutions and hospitals because of the different languages used. There is a need to develop and use common ontology to improve libraries of annotated surgical videos.

Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature

The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to provide answers to questions in natural language. The work contains best practices for acquiring scientific publications, tuning language models to identify and normalize relevant entities, creating representational models based on probabilistic topics, and formalizing an ontology that describes the associations between domain concepts supported by the scientific literature. All the resources generated in the domain of coronavirus are available openly as part of the Drugs4COVID initiative, and can be (re)-used independently or as a whole. They can be exploited by scientific communities conducting research related to SARS-CoV-2/COVID-19 and also by therapeutic communities, laboratories, etc., wishing to find and understand relationships between symptoms, drugs, active ingredients and their documentary evidence.

Graph data science and machine learning for the detection of COVID-19 infection from symptoms

Background

COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms.

Methods

The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted.

Results

We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy.

Conclusion

The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

SGMFQP: An ontology-based Swine Gut Microbiota Federated Query Platform

Gut microbiota plays a crucial role in modulating pig development and health, and gut microbiota characteristics are associated with differences in feed efficiency. To answer open questions in feed efficiency analysis, biologists seek to retrieve information across multiple heterogeneous data sources. However, this is error-prone and time-consuming work since the queries can involve a sequence of multiple sub-queries over several databases. We present an implementation of an ontology-based Swine Gut Microbiota Federated Query Platform (SGMFQP) that provides a convenient, automated, and efficient query service about swine feeding and gut microbiota. The system is constructed based on a domain-specific Swine Gut Microbiota Ontology (SGMO), which facilitates the construction of queries independent of the actual organization of the data in the individual sources. This process is supported by a template-based query interface. A Datalog⁺-based federated query engine transforms the queries into sub-queries tailored for each individual data source, and an automated workflow orchestration mechanism executes the queries in each source database and consolidates the results. The efficiency of the system is demonstrated on several swine feeding scenarios.

Content and quality of physical activity ontologies: a systematic review

INTRODUCTION

Ontologies are a formal way to represent knowledge in a particular field and have the potential to transform the field of health promotion and digital interventions. However, few researchers in physical activity (PA) are familiar with ontologies, and the field can be difficult to navigate. This systematic review aims to (1) identify ontologies in the field of PA, (2) assess their content and (3) assess their quality.

METHODS

Databases were searched for ontologies on PA. Ontologies were included if they described PA or sedentary behavior, and were available in English language. We coded whether ontologies covered the user profile, activity, or context domain. For the assessment of quality, we used 12 criteria informed by the Open Biological and Biomedical Ontology (OBO) Foundry principles of good ontology practice.

RESULTS

Twenty-eight ontologies met the inclusion criteria. All ontologies covered PA, and 19 included information on the user profile. Context was covered by 17 ontologies (physical context, n = 12; temporal context, n = 14; social context: n = 5). Ontologies met an average of 4.3 out of 12 quality criteria. No ontology met all quality criteria.

DISCUSSION

This review did not identify a single comprehensive ontology of PA that allowed reuse. Nonetheless, several ontologies may serve as a good starting point for the promotion of PA. We provide several recommendations about the identification, evaluation, and adaptation of ontologies for their further development and use.

Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises.

Love and romantic relationship in the domain of medicine

In this paper, I explore the nature of medical interventions like neuromodulation on the complex human experience of love. Love is built upon two fundamental natures, viz: the biological and the psychosocial. As a result of this distinction, scientists, and bioethicists have been exploring the possible ways this complex human experience can be biologically tampered with to produce some supposed higher-order ends like well-being and human flourishing. At the forefront in this quest are Earp, Sandberg and Savulescu whose research works over ten years has focused on the good that could stem from the medicalization of love. I acknowledge the various criticisms that have been made against this stance. However, most of these criticisms have been directed towards the mere side effects and sociocultural disservices that could result from the process of using drugs to influence human romantic relationships and in the end, critiques endorse the medicalization of love on the basis that its benefits outweigh the disadvantages. Consequently, I advance two strands of arguments against "medically-assisted love," the ontological and the socio-ethical arguments. The former presupposes that beyond the possible side effects of medicalizing love there is something inherently mistaken about this effort and there is something intrinsically different about love that distinguishes it from its medically-engineered alternative. In the latter argument, I claim that drug interventions in romantic love contravene the very nature of medicine. Overall, I believe that critiques were still able to endorse medicalizing love despite their objections because they were only looking at one direction, the physical/cultural complications.

Supporting SNOMED CT postcoordination with knowledge graph embeddings

SNOMED CT postcoordination is an underused mechanism that can help to implement advanced systems for the automatic extraction and encoding of clinical information from text. It allows defining non-existing SNOMED CT concepts by their relationships with existing ones. Manually building postcoordinated expressions is a difficult task. It requires a deep knowledge of the terminology and the support of specialized tools that barely exist. In order to support the building of postcoordinated expressions, we have implemented KGE4SCT: a method that suggests the corresponding SNOMED CT postcoordinated expression for a given clinical term. We leverage on the SNOMED CT ontology and its graph-like structure and use knowledge graph embeddings (KGEs). The objective of such embeddings is to represent in a vector space knowledge graph components (e.g. entities and relations) in a way that captures the structure of the graph. Then, we use vector similarity and analogies for obtaining the postcoordinated expression of a given clinical term. We obtained a semantic type accuracy of 98%, relationship accuracy of 90%, and analogy accuracy of 60%, with an overall completeness of postcoordination of 52% for the Spanish SNOMED CT version. We have also applied it to the English SNOMED CT version and outperformed state of the art methods in both, corpus generation for language model training for this task (improvement of 6% for analogy accuracy), and automatic postcoordination of SNOMED CT expressions, with an increase of 17% for partial conversion rate.

Proposal and multicentric validation of a laparoscopic Roux-en-Y gastric bypass surgery ontology

BACKGROUND

Phase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack external validation. To overcome these limitations an ontology for phases and steps of laparoscopic Roux-en-Y gastric bypass (LRYGB) is proposed and validated on a multicentric dataset in terms of inter- and intra-rater reliability (inter-/intra-RR).

METHODS

The proposed LRYGB ontology consists of 12 phase and 46 step definitions that are hierarchically structured. Two board certified surgeons (raters) with > 10 years of clinical experience applied the proposed ontology on two datasets: (1) StraBypass40 consists of 40 LRYGB videos from Nouvel Hôpital Civil, Strasbourg, France and (2) BernBypass70 consists of 70 LRYGB videos from Inselspital, Bern University Hospital, Bern, Switzerland. To assess inter-RR the two raters' annotations of ten randomly chosen videos from StraBypass40 and BernBypass70 each, were compared. To assess intra-RR ten randomly chosen videos were annotated twice by the same rater and annotations were compared. Inter-RR was calculated using Cohen's kappa. Additionally, for inter- and intra-RR accuracy, precision, recall, F1-score, and application dependent metrics were applied.

RESULTS

The mean ± SD video duration was 108 ± 33 min and 75 ± 21 min in StraBypass40 and BernBypass70, respectively. The proposed ontology shows an inter-RR of 96.8 ± 2.7% for phases and 85.4 ± 6.0% for steps on StraBypass40 and 94.9 ± 5.8% for phases and 76.1 ± 13.9% for steps on BernBypass70. The overall Cohen's kappa of inter-RR was 95.9 ± 4.3% for phases and 80.8 ± 10.0% for steps. Intra-RR showed an accuracy of 98.4 ± 1.1% for phases and 88.1 ± 8.1% for steps.

CONCLUSION

The proposed ontology shows an excellent inter- and intra-RR and should therefore be implemented routinely in phase and step annotation of LRYGB.