Front Neuroinform. 2023 Jul 18;17:1174156. doi: 10.3389/fninf.2023.1174156. eCollection 2023.

ABSTRACT

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

PMID:37533796 | PMC:PMC10392125 | DOI:10.3389/fninf.2023.1174156

Biomed Eng Online. 2023 Jul 31;22(1):76. doi: 10.1186/s12938-023-01138-3.

ABSTRACT

BACKGROUND: In the future, extended reality technology will be widely used. People will be led to utilize virtual reality (VR) and augmented reality (AR) technologies in their daily lives, hobbies, numerous types of entertainment, and employment. Medical augmented reality has evolved with applications ranging from medical education to picture-guided surgery. Moreover, a bulk of research is focused on clinical applications, with the majority of research devoted to surgery or intervention, followed by rehabilitation and treatment applications. Numerous studies have also looked into the use of augmented reality in medical education and training.

METHODS: Using the databases Semantic Scholar, Web of Science, Scopus, IEEE Xplore, and ScienceDirect, a scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. To find other articles, a manual search was also carried out in Google Scholar. This study presents studies carried out over the previous 14 years (from 2009 to 2023) in detail. We classify this area of study into the following categories: (1) AR and VR in surgery, which is presented in the following subsections: subsection A: MR in neurosurgery; subsection B: spine surgery; subsection C: oral and maxillofacial surgery; and subsection D: AR-enhanced human-robot interaction; (2) AR and VR in medical education presented in the following subsections; subsection A: medical training; subsection B: schools and curriculum; subsection C: XR in Biomedicine; (3) AR and VR for rehabilitation presented in the following subsections; subsection A: stroke rehabilitation during COVID-19; subsection B: cancer and VR, and (4) Millimeter-wave and MIMO systems for AR and VR.

RESULTS: In total, 77 publications were selected based on the inclusion criteria. Four distinct AR and/or VR applications groups could be differentiated: AR and VR in surgery (N = 21), VR and AR in Medical Education (N = 30), AR and VR for Rehabilitation (N = 15), and Millimeter-Wave and MIMO Systems for AR and VR (N = 7), where N is number of cited studies. We found that the majority of research is devoted to medical training and education, with surgical or interventional applications coming in second. The research is mostly focused on rehabilitation, therapy, and clinical applications. Moreover, the application of XR in MIMO has been the subject of numerous research.

CONCLUSION: Examples of these diverse fields of applications are displayed in this review as follows: (1) augmented reality and virtual reality in surgery; (2) augmented reality and virtual reality in medical education; (3) augmented reality and virtual reality for rehabilitation; and (4) millimeter-wave and MIMO systems for augmented reality and virtual reality.

PMID:37525193 | DOI:10.1186/s12938-023-01138-3

Br J Gen Pract. 2023 Aug 31;73(734):e694-e701. doi: 10.3399/BJGP.2022.0592. Print 2023 Sep.

ABSTRACT

BACKGROUND: Urinary tract infections (UTIs) are a common indication for antibiotic prescriptions, reductions in which would reduce antimicrobial resistance (AMR). Risk stratification of patients allows reductions to be made safely.

AIM: To identify risk factors for hospital admission following UTI, to inform targeted antibiotic stewardship.

DESIGN AND SETTING: Retrospective cohort study of East London primary care patients.

METHOD: Hospital admission outcomes following primary care consultation for UTI were analysed using linked data from primary care, secondary care, and microbiology, from 1 April 2012 to 31 March 2017. The outcomes analysed were urinary infection-related hospital admission (UHA) and all-cause hospital admission (AHA) within 30 days of UTI in primary care. Odds ratios between specific variables (demographic characteristics, prior antibiotic exposure, and comorbidities) and the outcomes were predicted using generalised estimating equations, and fitted to a final multivariable model including all variables with a P-value <0.1 on univariable analysis.

RESULTS: Of the 169 524 episodes of UTI, UHA occurred in 1336 cases (0.8%, 95% confidence interval [CI] = 0.7 to 0.8) and AHA in 6516 cases (3.8%, 95% CI = 3.8 to 3.9). On multivariable analysis, increased odds of UHA were seen in patients aged 55-74 years (adjusted odds ratio [AOR] 1.49) and ≥75 years (AOR 3.24), relative to adults aged 16-34 years. Increased odds of UHA were also associated with chronic kidney disease (CKD; AOR 1.55), urinary catheters (AOR 2.01), prior antibiotics (AOR 1.38 for ≥3 courses), recurrent UTI (AOR 1.33), faecal incontinence (FI; AOR 1.47), and diabetes mellitus (DM; AOR 1.37).

CONCLUSION: Urinary infection-related hospital admission after primary care consultation for community-onset lower UTI was rare; however, increased odds for UHA were observed for some patient groups. Efforts to reduce antibiotic prescribing for suspected UTI should focus on patients aged <55 years without risk factors for complicated UTI, recurrent UTI, DM, or FI.

PMID:37487642 | PMC:PMC10394611 | DOI:10.3399/BJGP.2022.0592

J Biomed Semantics. 2023 Jul 18;14(1):8. doi: 10.1186/s13326-023-00285-9.

ABSTRACT

BACKGROUND: Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding guideline recommendations into decision support systems, much of the previous work has not focused on enabling system generated recommendations through the formalization of changes in a guideline, the provenance of a recommendation, and applicability of the evidence. Prior work indicates that healthcare providers may not find that guideline-derived recommendations always meet their needs for reasons such as lack of relevance, transparency, time pressure, and applicability to their clinical practice.

RESULTS: We introduce several semantic techniques that model diseases based on clinical practice guidelines, provenance of the guidelines, and the study cohorts they are based on to enhance the capabilities of clinical decision support systems. We have explored ways to enable clinical decision support systems with semantic technologies that can represent and link to details in related items from the scientific literature and quickly adapt to changing information from the guidelines, identifying gaps, and supporting personalized explanations. Previous semantics-driven clinical decision systems have limited support in all these aspects, and we present the ontologies and semantic web based software tools in three distinct areas that are unified using a standard set of ontologies and a custom-built knowledge graph framework: (i) guideline modeling to characterize diseases, (ii) guideline provenance to attach evidence to treatment decisions from authoritative sources, and (iii) study cohort modeling to identify relevant research publications for complicated patients.

CONCLUSIONS: We have enhanced existing, evidence-based knowledge by developing ontologies and software that enables clinicians to conveniently access updates to and provenance of guidelines, as well as gather additional information from research studies applicable to their patients' unique circumstances. Our software solutions leverage many well-used existing biomedical ontologies and build upon decades of knowledge representation and reasoning work, leading to explainable results.

PMID:37464259 | DOI:10.1186/s13326-023-00285-9

Data Brief. 2023 Jul 5;49:109375. doi: 10.1016/j.dib.2023.109375. eCollection 2023 Aug.

ABSTRACT

The recovery and resilience of the cultural and creative sectors after the COVID-19 pandemic is a current topic with priority for the European Commission. Cultural gems is a crowdsourced web platform managed by the Joint Research Centre of the European Commission aimed at creating community-led maps as well as a common repository for cultural and creative places across European cities and towns. More than 130,000 physical locations and online cultural activities in more than 300 European cities and towns are currently tracked by the application. The main objective of Cultural gems consists in raising a holistic vision of European culture, reinforcing a sense of belonging to a common European cultural space. This data article describes the ontology developed for Cultural gems, adopted to represent the domain of knowledge of the application by means of FAIR (Findable, Accessible, Interoperable, Reusable) principles and following the paradigms of Linked Open Data (LOD). We provide an overview of this dataset, and describe the ontology model, along with the services used to access and consume the data.

PMID:37456121 | PMC:PMC10339159 | DOI:10.1016/j.dib.2023.109375

J Behav Addict. 2023 Jul 14. doi: 10.1556/2006.2023.00035. Online ahead of print.

ABSTRACT

Implicit cognitions may be involved in the development and maintenance of specific Internet use disorders such as problematic social network use (PSNU). In more detail, implicit attitude, attentional biases, approach and avoidance tendencies as well as semantic memory associations are considered relevant in the context of PSNU. This viewpoint article summarizes the available literature on implicit cognitions in PSNU. We systematically reviewed articles of implicit cognitions in PSNU from PubMed, Scopus, Web of Science, and ProQuest databases based on a targeted search strategy and assessed using predefined inclusion and exclusion criteria. The present findings suggest that specific implicit cognitions are important in the context of PSNU and therefore show parallels to other addictive behaviors. However, the empirical evidence is limited to a few studies on this topic. Implicit cognitions in PSNU should be explored in more depth and in the context of other affective and cognitive mechanisms in future work.

PMID:37450371 | DOI:10.1556/2006.2023.00035

PLoS One. 2023 Jul 10;18(7):e0283809. doi: 10.1371/journal.pone.0283809. eCollection 2023.

ABSTRACT

Image transfer plays a significant role in the manufacture of PCB; it affects the production speed and quality of the manufacturing process. This study proposes a surface-framework structure, which divides the network into two parts: surface and framework. The surface part does not include subsampling to extract the detailed features of the image, thereby improving the segmentation effect when the computing power requirement is not large. Meanwhile, a semantic segmentation method based on Unet and surface-framework structure, called pure efficient Unet (PE Unet), is proposed. A comparative experiment is conducted on our mark-point dataset (MPRS). The proposed model achieved good results in various metrics. The proposed network's IoU attained 84.74%, which is 3.15% higher than Unet. The GFLOPs is 34.0 which shows that the network model balances performance and speed. Furthermore, comparative experiments on MPRS, CHASE_DB1, TCGA-LGG datasets for Surface-Framework structure are introduced, the IoU promotion clipped means on these datasets are 2.38%, 4.35% and 0.78% respectively. The Surface-Framework structure can weaken the gridding effect and improve the performance of semantic segmentation network.

PMID:37428717 | PMC:PMC10332590 | DOI:10.1371/journal.pone.0283809

ACM Trans Access Comput. 2023 Jun;16(2):1-26. doi: 10.1145/3590775. Epub 2023 May 11.

ABSTRACT

In this article, we introduce Semantic Interior Mapology (SIM), a web app that allows anyone to quickly trace the floor plan of a building, generating a vectorized representation that can be automatically converted into a tactile map at the desired scale. The design of SIM is informed by a focus group with seven blind participants. Maps generated by SIM at two different scales have been tested by a user study with 10 participants, who were asked to perform a number of tasks designed to ascertain the spatial knowledge acquired through map exploration. These tasks included cross-map pointing and path finding, and determination of turn direction/walker orientation during imagined path traversal. By and large, participants were able to successfully complete the tasks, suggesting that these types of maps could be useful for pre-journey spatial learning.

PMID:37427355 | PMC:PMC10327626 | DOI:10.1145/3590775

Technol Health Care. 2023 Jun 29. doi: 10.3233/THC-230237. Online ahead of print.

ABSTRACT

BACKGROUND: Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time.

OBJECTIVE: In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients.

METHODS: Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated.

RESULTS: After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients.

CONCLUSION: The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

PMID:37393457 | DOI:10.3233/THC-230237

Lancet Public Health. 2023 Jul;8(7):e535-e545. doi: 10.1016/S2468-2667(23)00098-1.

ABSTRACT

BACKGROUND: To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales.

METHODS: In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile).

FINDINGS: Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased.

INTERPRETATION: The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death.

FUNDING: Health Data Research UK.

PMID:37393092 | DOI:10.1016/S2468-2667(23)00098-1

J Biomed Semantics. 2023 Jul 1;14(1):7. doi: 10.1186/s13326-023-00289-5.

ABSTRACT

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a "Check" module providing a thorough metadata evaluation and recommendations, and an "Inspect" module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.

PMID:37393296 | DOI:10.1186/s13326-023-00289-5

Educ Inf Technol (Dordr). 2023 Mar 13:1-50. doi: 10.1007/s10639-023-11664-1. Online ahead of print.

ABSTRACT

Wikidata is a free, multilingual, open knowledge-base that stores structured, linked data. It has grown rapidly and as of December 2022 contains over 100 million items and millions of statements, making it the largest semantic knowledge-base in existence. Changing the interaction between people and knowledge, Wikidata offers various learning opportunities, leading to new applications in sciences, technology and cultures. These learning opportunities stem in part from the ability to query this data and ask questions that were difficult to answer in the past. They also stem from the ability to visualize query results, for example on a timeline or a map, which, in turn, helps users make sense of the data and draw additional insights from it. Research on the semantic web as learning platform and on Wikidata in the context of education is almost non-existent, and we are just beginning to understand how to utilize it for educational purposes. This research investigates the Semantic Web as a learning platform, focusing on Wikidata as a prime example. To that end, a methodology of multiple case studies was adopted, demonstrating Wikidata uses by early adopters. Seven semi-structured, in-depth interviews were conducted, out of which 10 distinct projects were extracted. A thematic analysis approach was deployed, revealing eight main uses, as well as benefits and challenges to engaging with the platform. The results shed light on Wikidata's potential as a lifelong learning process, enabling opportunities for improved Data Literacy and a worldwide social impact.

PMID:37361737 | PMC:PMC10009355 | DOI:10.1007/s10639-023-11664-1

Bioinform Adv. 2023 Jun 1;3(1):vbad067. doi: 10.1093/bioadv/vbad067. eCollection 2023.

ABSTRACT

SUMMARY: Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host-pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host-pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system.

AVAILABILITY AND IMPLEMENTATION: https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin.

PMID:37359724 | PMC:PMC10290227 | DOI:10.1093/bioadv/vbad067

PeerJ Comput Sci. 2023 Feb 10;9:e1227. doi: 10.7717/peerj-cs.1227. eCollection 2023.

ABSTRACT

3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according to freehand sketch. Researchers are focusing on applying machine learning technology to 3D model retrieval. In this article, we combine semantic feature, shape distribution features and gist feature to retrieve 3D model based on interactive attention convolutional neural networks (CNN). The purpose is to improve the accuracy of 3D model retrieval. Firstly, 2D (two-dimensional) views are extracted from 3D model at six different angles and converted into line drawings. Secondly, interactive attention module is embedded into CNN to extract semantic features, which adds data interaction between two CNN layers. Interactive attention CNN extracts effective features from 2D views. Gist algorithm and 2D shape distribution (SD) algorithm are used to extract global features. Thirdly, Euclidean distance is adopted to calculate the similarity of semantic feature, the similarity of gist feature and the similarity of shape distribution feature between sketch and 2D view. Then, the weighted sum of three similarities is used to compute the similarity between sketch and 2D view for retrieving 3D model. It solves the problem that low accuracy of 3D model retrieval is caused by the poor extraction of semantic features. Nearest neighbor (NN), first tier (FT), second tier (ST), F-measure (E(F)), and discounted cumulated gain (DCG) are used to evaluate the performance of 3D model retrieval. Experiments are conducted on ModelNet40 and results show that the proposed method is better than others. The proposed method is feasible in 3D model retrieval.

PMID:37346676 | PMC:PMC10280475 | DOI:10.7717/peerj-cs.1227

PeerJ Comput Sci. 2022 Sep 22;8:e1099. doi: 10.7717/peerj-cs.1099. eCollection 2022.

ABSTRACT

BACKGROUND: Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI.

PROBLEM DESCRIPTION: In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indicators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data.

PROPOSED SOLUTION: To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets.

RESULTS: The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.

PMID:37346315 | PMC:PMC10280256 | DOI:10.7717/peerj-cs.1099

J Cheminform. 2023 Jun 20;15(1):61. doi: 10.1186/s13321-023-00729-5.

ABSTRACT

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project.

PMID:37340506 | DOI:10.1186/s13321-023-00729-5

J Adolesc Health. 2023 Sep;73(3):519-526. doi: 10.1016/j.jadohealth.2023.04.018. Epub 2023 Jun 16.

ABSTRACT

PURPOSE: Youth homelessness remains an ongoing public health issue worldwide. We aimed to describe the burden of emergency department (ED) presentations and hospitalizations among a South Australian population of young people in contact with specialist homelessness services (SHS).

METHODS: This whole-of-population study used de-identified, linked administrative data from the Better Evidence Better Outcomes Linked Data (BEBOLD) platform on all individuals born between 1996 and 1998 (N = 57,509). The Homelessness2Home data collection was used to identify 2,269 young people in contact with SHS at ages 16-17 years. We followed these 57,509 individuals to age 18-19 years and compared ED presentations and hospital separations related to mental health, self-harm, drug and alcohol, injury, oral health, respiratory conditions, diabetes, pregnancy, and potentially preventable hospitalizations between those in contact and not in contact with SHS.

RESULTS: Four percent of young people had contact with SHS at ages 16-17 years. Young people who had contact with SHS were 2 and 3 times more likely to have presented to an ED and hospital respectively, compared to those who did not contact SHS. This accounted for 13% of all ED presentations and 16% of all hospitalizations in this age group. Excess burden causes included mental health, self-harm, drug and alcohol, diabetes, and pregnancy. On average, young people in contact with SHS experienced an increased length of stay in ED (+0.6 hours) and hospital (+0.7 days) per presentation, and were more likely to not wait for treatment in ED and to self-discharge from hospital.

DISCUSSION: The 4% of young people who contacted SHS at ages 16-17 years accounted for 13% and 16% of all ED presentations and hospitalizations respectively at age 18-19 years. Prioritizing access to stable housing and primary health-care services for adolescents in contact with SHS in Australia could improve health outcomes and reduce health-care costs.

PMID:37330707 | DOI:10.1016/j.jadohealth.2023.04.018

J Psychiatr Res. 2023 Aug;164:51-58. doi: 10.1016/j.jpsychires.2023.05.043. Epub 2023 May 23.

ABSTRACT

BACKGROUND: Premature mortality in people with mental illness is well-documented, yet deaths during inpatient psychiatric care have received little research attention. This study investigates mortality rates and causes of death during inpatient psychiatric care in New South Wales (NSW), Australia. Risk factors for inpatient death were also explored.

METHODS: A retrospective cohort study using linked administrative datasets with complete capture of psychiatric admissions in NSW from 2002 to 2012 (n = 421,580) was conducted. Univariate and multivariate random-effects logistic regression analyses were used to explore risk factors for inpatient death.

RESULTS: The mortality rate during inpatient psychiatric care was 1.12 deaths per 1000 episodes of care and appeared to decline over the study period. Suicide accounted for 17% of inpatient deaths, while physical health causes accounted for 75% of all deaths. Thirty percent of these deaths were considered potentially avoidable. In the multivariate model, male sex, unknown address and several physical health diagnoses were associated with increased deaths.

CONCLUSIONS: The mortality rate and number of avoidable deaths during inpatient psychiatric care were substantial and warrant further systemic investigation. This was driven by a dual burden of physical health conditions and suicide. Strategies to improve access to physical health care on psychiatric inpatient wards and prevent inpatient suicide are necessary. A coordinated approach to monitoring psychiatric inpatient deaths in Australia is not currently available and much needed.

PMID:37315354 | DOI:10.1016/j.jpsychires.2023.05.043

Palliat Med. 2023 Sep;37(8):1232-1240. doi: 10.1177/02692163231180912. Epub 2023 Jun 12.

ABSTRACT

BACKGROUND: Understanding patterns of health care use in the last year of life is critical in health services planning.

AIM: To describe hospital-based service and palliative care use in hospital in the year preceding death for patients who died of heart failure or cardiomyopathy in Queensland from 2008 to 2018 and had at least one hospitalisation in the year preceding death.

DESIGN: A retrospective data linkage study was conducted using administrative health data relating to hospitalisations, emergency department visits and deaths.

PARTICIPANTS AND SETTING: Participants included were those aged ⩾60 years, had a hospitalisation in their last year of life and died of heart failure or cardiomyopathy in Queensland, Australia.

RESULTS: Of the 4697 participants, there were 25,583 hospital admissions. Three quarters (n = 3420, 73%) of participants were aged ⩾80 years and over half died in hospital (n = 2886, 61%). The median number of hospital admissions in the last year of life was 3 (interquartile range [IQR] 2-5). The care type was recorded as 'acute' for 89% (n = 22,729) of hospital admissions, and few (n = 853, 3%) hospital admissions had a care type recorded as 'palliative.' Of the 4697 participants, 3458 had emergency department visit(s), presenting 10,330 times collectively.

CONCLUSION: In this study, patients who died of heart failure or cardiomyopathy were predominantly aged ⩾80 years and over half died in hospital. These patients experienced repeat acute hospitalisations in the year preceding death. Improving timely access to palliative care services in the outpatient or community setting is needed for patients with heart failure.

PMID:37306096 | DOI:10.1177/02692163231180912

Int J Environ Res Public Health. 2023 May 29;20(11):5984. doi: 10.3390/ijerph20115984.

ABSTRACT

Depression contributes to a wide range of maladjustment problems. With the development of technology, objective measurement for behavior and functional indicators of depression has become possible through the passive sensing technology of digital devices. Focusing on location data, we systematically reviewed the relationship between depression and location data. We searched Scopus, PubMed, and Web of Science databases by combining terms related to passive sensing and location data with depression. Thirty-one studies were included in this review. Location data demonstrated promising predictive power for depression. Studies examining the relationship between individual location data variables and depression, homestay, entropy, and the normalized entropy variable of entropy dimension showed the most consistent and significant correlations. Furthermore, variables of distance, irregularity, and location showed significant associations in some studies. However, semantic location showed inconsistent results. This suggests that the process of geographical movement is more related to mood changes than to semantic location. Future research must converge across studies on location-data measurement methods.

PMID:37297588 | DOI:10.3390/ijerph20115984