Stud Health Technol Inform. 2022 May 16;293:169-170. doi: 10.3233/SHTI220364.

ABSTRACT

Just-in-time adaptive interventions (JITAIs) can promote behavior change in patients. It was the aim of our study to make JITAIs adaptable, i.e., to configure JITAIs for different purposes and to personalize them for different participants, whilst enabling central maintenance and integrated data analysis across deployments and individuals. We present a concept for adaptable JITAIs that was created following a design science approach. It builds on multi-level conceptual modeling and knowledge graphs and will be evaluated in user studies.

PMID:35592977 | DOI:10.3233/SHTI220364

Stud Health Technol Inform. 2022 May 16;293:59-66. doi: 10.3233/SHTI220348.

ABSTRACT

Decision-making based on so-called medical guidelines supported by semantic AI solutions is an essential and significant task for medical personnel in both a pre-clinical setting and an inner-clinical environment. Semantic representations of medical guidelines and Fast Healthcare Interoperability Resources (FHIR) using Semantic Web technologies, i.e., Resource Description Framework (RDF), rules (RuleML and Prova), and Shape Constraint Language (SHACL), provide a semantic knowledge base for the decision-making process and ease technical implementation and automation tasks. Current medical decision support systems lack Semantic Web integration using FHIR-RDF representations as a data source. In this paper, we implement a particular medical guideline using two different approaches: Prova [8] and SHACL [13]. We generate a series of raw FHIR-data for a selected guideline, the ABCDE approach, and compare the implemented two programs' (Prova and SHACL) results. Both approaches deliver the same results in terms of content. Both may be used within a distributed medical environment depending on the need of organizations.

PMID:35592961 | DOI:10.3233/SHTI220348

FASEB J. 2022 May;36 Suppl 1. doi: 10.1096/fasebj.2022.36.S1.R5732.

ABSTRACT

OBJECTIVE: Our long-term goal is the development of a clinically-relevant, validated enterprise data warehouse for research. The Intergenerational Health Knowledgebase (IHK) will advance and accelerate the study of the developmental origins of adult disease. We sought to harmonize data from the electronic health record (EHR) and external data sources and efficiently synthesize key variables from pregnancy and early life in the IHK. The objective was to make the IHK be efficient, effective, flexible and able to integrate with participation information for on-campus biobanks.

METHODS: The development of the IHK is an iterative process involving multiple steps: 1) clinical data team (CDT) members identify and locate clinically significant distinct clinical variables in the front-facing EHR pertaining to obstetrics and pediatrics. 2) The Biomedical Informatics group in the Institute for Clinical and Translational Science (ICTS-BMI) group locates these variables in the transactional database of the EHR. 3) The CDT members validate the extracted variables. Extracted data is compared with expected outcomes from the EHR. Inconsistencies are reviewed by the CDT and ICTS-BMI group to pinpoint potential causes. 4) With confirmed data variables, ICTS-BMI develops novel data tables and user-friendly SQL queries. The resultant OBstetrics Data Integration Architecture structure integrates multiple data sources within the institution which includes, but is not limited to, imaging data, diagnoses, vital sign information, medications, medication administration, and procedures for the maternal-fetal dyad. Powering this architecture is a common semantic layer that uses established standardized vocabularies and value sets. This includes storing patient demographic data, patient diagnoses using ICD9-10 codes, storing laboratory results using LOINC codes, and problems using ICD9-10/SNOMED-CT codes. In our EHR, all data is joined using pregnancy episode identification numbers linking maternal and neonatal data under a singular pregnancy instance. ICTS-BMI developed a secure web-based Knowledgebase viewer for performing queries of the IHK without expert SQL knowledge.

RESULTS: The IHK contains indexed maternal and child data. The IHK is frequently updated to contain every pregnancy cared for at the University of Iowa Hospitals & Clinics (UIHC) (currently 62,000+ pregnancies). Because of our team's complementary expertise, they were able to anticipate needed information, identify and validate key variables in the EMR, and plan pre-defined queries. As a result, we have quickly provided data for studies ranging from the effects of maternal environmental exposures, the role of patient distance to the hospital in pregnancy outcomes, medication use during pregnancy, and the association of depression with adverse outcomes.

CONCLUSION: The development of the novel IHK has facilitated pregnancy-related research and identified new targets for improving maternal health and following long-term health outcomes of parents and children. Further, integration of the IHK with the UIHC Women's Health Tissue Repository facilitates using biological specimens for research.

PMID:35553641 | DOI:10.1096/fasebj.2022.36.S1.R5732

J Med Internet Res. 2022 May 11;24(5):e36086. doi: 10.2196/36086.

ABSTRACT

BACKGROUND: Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.

OBJECTIVE: We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.

METHODS: The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.

RESULTS: The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.

CONCLUSIONS: Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

PMID:35544307 | DOI:10.2196/36086

Nucleic Acids Res. 2022 May 10:gkac310. doi: 10.1093/nar/gkac310. Online ahead of print.

ABSTRACT

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

PMID:35536289 | DOI:10.1093/nar/gkac310

J Biomed Semantics. 2022 May 8;13(1):13. doi: 10.1186/s13326-022-00269-1.

ABSTRACT

BACKGROUND: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field.

METHODS: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations.

RESULTS: This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores.

CONCLUSION: The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

PMID:35527259 | DOI:10.1186/s13326-022-00269-1

Int J Popul Data Sci. 2022 Apr 27;7(1):1732. doi: 10.23889/ijpds.v6i1.1732. eCollection 2022.

ABSTRACT

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

PMID:35520098 | PMC:PMC9052959 | DOI:10.23889/ijpds.v6i1.1732

Genes Genomics. 2022 May 5. doi: 10.1007/s13258-022-01245-w. Online ahead of print.

ABSTRACT

BACKGROUND: Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.

OBJECTIVE: Use of reduced vector to represent transcriptome data independent of platforms.

METHODS: Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.

RESULTS: Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.

CONCLUSIONS: TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .

PMID:35511320 | DOI:10.1007/s13258-022-01245-w

Subst Abus. 2022;43(1):1100-1109. doi: 10.1080/08897077.2022.2060445.

ABSTRACT

Background: Homelessness and substance use are intricately related, and both are prevalent among emergency department (ED) patients. This study examined the longitudinal association of substance use characteristics with future homeless shelter entry among ED patients with any drug use or unhealthy alcohol use. Methods: We present results from a longitudinal cohort study of public hospital ED patients who screened positive for drug use or unhealthy alcohol use and who were not homeless at their baseline (index) ED visit. The primary outcome was homeless shelter entry within 12 months of baseline, ascertained in city homeless shelter administrative data. Primary independent variables of interest were alcohol use severity (AUDIT), drug use severity (DAST-10), and types of drugs used, as reported on baseline survey questionnaires. Results: Analyses included 1,210 ED patients. By 12 months following the baseline ED visit, 114 (9.4%) had entered a homeless shelter. Among patients with the most severe problems related to drug use (DAST-10 score 9-10), 40.9% entered a shelter within 12 months. Past shelter use was the strongest predictor of future shelter entry; once adjusting for historic shelter use the relationship of AUDIT and DAST-10 scores with future shelter entry was no longer statistically significant in multivariable models. Conclusions: ED patients with past year drug use or unhealthy alcohol use had relatively high likelihood of future shelter entry. Risk for homelessness should be addressed in future interventions with this population. Findings illustrate the complexity of relationships between substance use and homelessness.

PMID:35499455 | DOI:10.1080/08897077.2022.2060445

Front Psychol. 2022 Apr 14;13:805792. doi: 10.3389/fpsyg.2022.805792. eCollection 2022.

ABSTRACT

The objective of this article was to review existing research to assess the evidence for predictive processing (PP) in sign language, the conditions under which it occurs, and the effects of language mastery (sign language as a first language, sign language as a second language, bimodal bilingualism) on the neural bases of PP. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. We searched peer-reviewed electronic databases (SCOPUS, Web of Science, PubMed, ScienceDirect, and EBSCO host) and gray literature (dissertations in ProQuest). We also searched the reference lists of records selected for the review and forward citations to identify all relevant publications. We searched for records based on five criteria (original work, peer-reviewed, published in English, research topic related to PP or neural entrainment, and human sign language processing). To reduce the risk of bias, the remaining two authors with expertise in sign language processing and a variety of research methods reviewed the results. Disagreements were resolved through extensive discussion. In the final review, 7 records were included, of which 5 were published articles and 2 were dissertations. The reviewed records provide evidence for PP in signing populations, although the underlying mechanism in the visual modality is not clear. The reviewed studies addressed the motor simulation proposals, neural basis of PP, as well as the development of PP. All studies used dynamic sign stimuli. Most of the studies focused on semantic prediction. The question of the mechanism for the interaction between one's sign language competence (L1 vs. L2 vs. bimodal bilingual) and PP in the manual-visual modality remains unclear, primarily due to the scarcity of participants with varying degrees of language dominance. There is a paucity of evidence for PP in sign languages, especially for frequency-based, phonetic (articulatory), and syntactic prediction. However, studies published to date indicate that Deaf native/native-like L1 signers predict linguistic information during sign language processing, suggesting that PP is an amodal property of language processing.

SYSTEMATIC REVIEW REGISTRATION: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238911], identifier [CRD42021238911].

PMID:35496220 | PMC:PMC9047358 | DOI:10.3389/fpsyg.2022.805792

PeerJ Comput Sci. 2022 Mar 10;8:e921. doi: 10.7717/peerj-cs.921. eCollection 2022.

ABSTRACT

Scientific data management plays a key role in the reproducibility of scientific results. To reproduce results, not only the results but also the data and steps of scientific experiments must be made findable, accessible, interoperable, and reusable. Tracking, managing, describing, and visualizing provenance helps in the understandability, reproducibility, and reuse of experiments for the scientific community. Current systems lack a link between the data, steps, and results from the computational and non-computational processes of an experiment. Such a link, however, is vital for the reproducibility of results. We present a novel solution for the end-to-end provenance management of scientific experiments. We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility), which allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational data and steps in an interoperable way. CAESAR integrates the REPRODUCE-ME provenance model, extended from existing semantic web standards, to represent the whole picture of an experiment describing the path it took from its design to its result. ProvBook, an extension for Jupyter Notebooks, is developed and integrated into CAESAR to support computational reproducibility. We have applied and evaluated our contributions to a set of scientific experiments in microscopy research projects.

PMID:35494870 | PMC:PMC9044346 | DOI:10.7717/peerj-cs.921

Child Adolesc Psychiatry Ment Health. 2022 Apr 27;16(1):31. doi: 10.1186/s13034-022-00465-y.

ABSTRACT

INTRODUCTION: Children and adolescents living with HIV (C/ALHIV) are at a risk for significant neurocognitive deficits. There is limited literature that addresses the role of socioeconomic factors in neurocognitive deficits among CALHIV in Sub Saharan Africa (SSA), as it is very difficult to establish this causal relationship. Our systematic review was guided by the biodevelopmental framework that assumes that foundations of health and adversity affect later development and life outcomes. This systematic review aims to assess available evidence on the relationship between neurocognitive deficits and socioeconomic factors among HIV children and adolescents in SSA region.

METHOD: Using a pre-determined search strategy, we searched electronic databases including PubMed, web of Science and EBSCOhost (CINAHL and MEDLINE). Peer-reviewed publications that address neurocognitive deficits, psychosocial and socioeconomic risk factors among children and adolescents living with HIV in SSA were included in review.

RESULTS: Out of 640 articles, 17 studies from SSA met the inclusion criteria. Four studies reported no significant differences in the neurocognitive measures comparing children and adolescents with HIV infection to those uninfected. However, 10 studies suggest that C/ALHIV scored significantly low in general intellectual functions as compared to their uninfected peers. C/ALHIV were found to have substantial deficits in specific cognitive domains such as sequential processing, simultaneous processing, and learning. In addition, deficits in visuo-spatial processing, visual memory and semantic fluency were mentioned. Socioeconomic factors such as lower socioeconomic status (income, education and occupation), child orphanhood status and under-nutrition were linked with neurocognitive deficits.

CONCLUSION: Our findings suggest that CALHIV presented with poorer neurocognitive outcomes when compared to other populations which were associated with specific socioeconomic factors.

PMID:35477577 | DOI:10.1186/s13034-022-00465-y

J Epidemiol Community Health. 2022 Jul;76(7):646-652. doi: 10.1136/jech-2021-218415. Epub 2022 Apr 25.

ABSTRACT

BACKGROUND: The UK began an ambitious COVID-19 vaccination programme on 8 December 2020. This study describes variation in vaccination uptake by sociodemographic characteristics between December 2020 and August 2021.

METHODS: Using population-level administrative records linked to the 2011 Census, we estimated monthly first dose vaccination rates by age group and sociodemographic characteristics among adults aged 18 years or over in England. We also present a tool to display the results interactively.

RESULTS: Our sample included 35 223 466 adults. A lower percentage of males than females were vaccinated in the young and middle age groups (18-59 years) but not in the older age groups. Vaccination rates were highest among individuals of White British and Indian ethnic backgrounds and lowest among Black Africans (aged ≥80 years) and Black Caribbeans (18-79 years). Differences by ethnic group emerged as soon as vaccination roll-out commenced and widened over time. Vaccination rates were also lower among individuals who identified as Muslim, lived in more deprived areas, reported having a disability, did not speak English as their main language, lived in rented housing, belonged to a lower socioeconomic group, and had fewer qualifications.

CONCLUSION: We found inequalities in COVID-19 vaccination uptake rates by sex, ethnicity, religion, area deprivation, disability status, English language proficiency, socioeconomic position and educational attainment, but some of these differences varied by age group. Research is urgently needed to understand why these inequalities exist and how they can be addressed.

PMID:35470259 | DOI:10.1136/jech-2021-218415

J Biomed Semantics. 2022 Apr 25;13(1):12. doi: 10.1186/s13326-022-00263-7.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.

RESULTS: In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.

CONCLUSIONS: Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

PMID:35468846 | DOI:10.1186/s13326-022-00263-7

Diagnostics (Basel). 2022 Apr 2;12(4):887. doi: 10.3390/diagnostics12040887.

ABSTRACT

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses' infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application's primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.

PMID:35453935 | DOI:10.3390/diagnostics12040887

JMIR Form Res. 2022 Apr 21;6(4):e34962. doi: 10.2196/34962.

ABSTRACT

BACKGROUND: Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts.

OBJECTIVE: The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON.

METHODS: By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy.

RESULTS: DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results.

CONCLUSIONS: Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

PMID:35451991 | DOI:10.2196/34962

IEEE Trans Pattern Anal Mach Intell. 2022 Apr 19;PP. doi: 10.1109/TPAMI.2022.3168530. Online ahead of print.

ABSTRACT

This article studies the problem of learning weakly supervised semantic segmentation (WSSS) from image-level supervision only. Rather than previous efforts that primarily focus on intra-image information, we address the value of cross-image semantic relations for comprehensive object pattern mining. To achieve this, two neural co-attentions are incorporated into the classifier to complimentarily capture cross-image semantic similarities and differences. In particular, given a pair of training images, one co-attention enforces the classifier to recognize the common semantics from co-attentive objects, while the other one, called contrastive co-attention, drives the classifier to identify the unique semantics from the rest, unshared objects. This helps the classifier discover more object patterns and better ground semantics in image regions. More importantly, our algorithm provides a unified framework that handles well different WSSS settings, i.e., learning WSSS with (1) precise image-level supervision only, (2) extra simple single-label data, and (3) extra noisy web data. Without bells and whistles, it sets new state-of-the-arts on all these settings. Moreover, our approach ranked 1 st place in the WSSS Track of CVPR2020 LID Challenge. The extensive experimental results demonstrate well the efficacy and high utility of our method.

PMID:35439127 | DOI:10.1109/TPAMI.2022.3168530

Front Public Health. 2022 Mar 29;10:834172. doi: 10.3389/fpubh.2022.834172. eCollection 2022.

ABSTRACT

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

PMID:35425756 | PMC:PMC9002348 | DOI:10.3389/fpubh.2022.834172

Iran J Nurs Midwifery Res. 2022 Mar 14;27(2):83-91. doi: 10.4103/ijnmr.ijnmr_156_21. eCollection 2022 Mar-Apr.

ABSTRACT

BACKGROUND: Despite the importance and prominent role as a clinical, theoretical, and research approach in nursing practice, humanistic care nature and boundaries are not explicit and challenging for nurses to understand. This study was conducted to clarify the concept of humanistic care in nursing.

MATERIALS AND METHODS: Based on Rogers's evolutionary concept analysis, keywords such as "humanistic car *," "caring behave *," "humanistic nurs *," "humanistic model of care," were searched in PubMed, SCOPUS, Science Direct, Web of Science, WILEY, Springer, SAGE, ProQuest, SID, Iranmedex without time limit until November 2018. Sixty-five documents in nursing and ten documents in the medical discipline were finalized for thematic analysis.

RESULTS: Nine attributes of the humanistic care, including "excellence in clinical literacy," "creating a healing environment," "a comprehensive and unique viewpoint," "contribution to clients' adaptation and flourishing of their talents," "unrequited love and affection," "preservation of human dignity," "real presence," "constructive dynamic interaction," and "nurse's self-care," were recognized. Assessing the historical and evolutionary course of the concept's semantic tendency revealed three periods: The focus in first, second, and third was on the nurse-patient relationship, quantitative tendency/measurement, and metaphysics/spiritual humanism, respectively. The comparison of interdisciplinary differences indicated greater semantic comprehensiveness and depth in the nursing discipline.

CONCLUSIONS: Clear and practical definition and identification of humanistic care in nursing can be helpful in the further development of existing knowledge, instrumentation, designing guidelines, clinical interventions, knowledge translation, and correction of concept misuse. The identified antecedents and consequences can be in various aspects of clinical management.

PMID:35419263 | PMC:PMC8997180 | DOI:10.4103/ijnmr.ijnmr_156_21

Comput Biol Med. 2022 Mar 1;145:105313. doi: 10.1016/j.compbiomed.2022.105313. Online ahead of print.

ABSTRACT

Rare disease data is often fragmented within multiple heterogeneous siloed regional disease registries, each containing a small number of cases. These data are particularly sensitive, as low subject counts make the identification of patients more likely, meaning registries are not inclined to share subject level data outside their registries. At the same time access to multiple rare disease datasets is important as it will lead to new research opportunities and analysis over larger cohorts. To enable this, two major challenges must therefore be overcome. The first is to integrate data at a semantic level, so that it is possible to query over registries and return results which are comparable. The second is to enable queries which do not take subject level data from the registries. To meet the first challenge, this paper presents the FAIRVASC ontology to manage data related to the rare disease anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV), which is based on the harmonisation of terms in seven European data registries. It has been built upon a set of key clinical questions developed by a team of experts in vasculitis selected from the registry sites and makes use of several standard classifications, such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Orphacode. It also presents the method for adding semantic meaning to AAV data across the registries using the declarative Relational to Resource Description Framework Mapping Language (R2RML). To meet the second challenge a federated querying approach is presented for accessing aggregated and pseudonymized data, and which supports analysis of AAV data in a manner which protects patient privacy. For additional security the federated querying approach is augmented with a method for auditing queries (and the uplift process) using the provenance ontology (PROV-O) to track when queries and changes occur and by whom. The main contribution of this work is the successful application of semantic web technologies and federated queries to provide a novel infrastructure that can readily incorporate additional registries, thus providing access to harmonised data relating to unprecedented numbers of patients with rare disease, while also meeting data privacy and security concerns.

PMID:35405400 | DOI:10.1016/j.compbiomed.2022.105313