Int J Lang Commun Disord. 2021 Jun 16. doi: 10.1111/1460-6984.12644. Online ahead of print.

ABSTRACT

BACKGROUND: Prenatal alcohol exposure (PAE) is associated with growth deficits and neurodevelopmental impairment including foetal alcohol spectrum disorder (FASD). Difficulties with oral and written communication skills are common among children with PAE; however, less is known about how communication skills of adolescents who have PAE compare with those who do not. Adolescence is a critical time for development, supporting the transition into adulthood, but it is considered a high-risk period for those with FASD.

AIMS: We conducted a systematic review to synthesize evidence regarding oral and written communication skills of adolescents with PAE or FASD and how they compare with those with no PAE.

METHODS & PROCEDURES: A comprehensive search strategy used seven databases: Cochrane Library, Cinahl, Embase, Medline, PsycInfo, Eric and Web of Science. Included studies reported on at least one outcome related to oral and written communication for a PAE (or FASD) group as well as a no/low PAE group, both with age ranges of 10-24 years. Quality assessment was undertaken.

MAIN CONTRIBUTION: Communication skills most often assessed in the seven studies included in this review were semantic knowledge, semantic processing, and verbal learning and memory. These communication skills, in addition to reading and spelling, were commonly weaker among adolescents with PAE compared with those with no/low PAE. However, the findings were inconsistent across studies, and studies differed in their methodologies.

CONCLUSIONS & IMPLICATIONS: Our results emphasize that for adolescents with PAE, communication skills in both oral and written modalities should be comprehensively understood in assessment and when planning interventions. A key limitation of the existing literature is that comparison groups often include some participants with a low level of PAE, and that PAE definitions used to allocate participants to groups differ across studies.

WHAT THIS PAPER ADDS: What is already known on the subject PAE and FASD are associated with deficits in oral and written communication skills. Studies to date have mostly focused on children with a FASD diagnosis as well as combined groups of children and adolescents with FASD or PAE. There is a gap in what is known about oral and written communication skills of adolescents, specifically, who have PAE or FASD. This has implications for the provision of assessment and supports during a period of increased social and academic demands. What this study adds to existing knowledge This review provides systematic identification, assessment and synthesis of the current literature related to oral and written communication skills of adolescents with PAE compared with those with no/low PAE. The review revealed a small knowledge base with inconsistent methodologies and findings across studies. However, the findings overall highlight that adolescents with PAE have weaker skills in oral and written language than those with no/low PAE. Results are discussed in relation to education, social and emotional well-being, and forensic contexts. What are the potential or actual clinical implications of this work? Findings emphasize that for adolescents with PAE, comprehensive assessment of both oral and written communication skills, through both standardized and functional tasks, should be undertaken. Speech-language pathologists have a key role in assessment with individuals who have PAE.

PMID:34137136 | DOI:10.1111/1460-6984.12644

J Ethnopharmacol. 2021 Jun 8:114313. doi: 10.1016/j.jep.2021.114313. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Glycosmis pentaphylla (Retz.) DC. is a perennial shrub indigenous to the tropical and subtropical regions of India, China, Sri Lanka, Myanmar, Bangladesh, Indonesia, Malaysia, Thailand, Vietnam, Philippine, Java, Sumatra, Borneo and Australia. The plant is used extensively within these regions as a traditional medicine for the treatment of a variety of ailments including cough, fever, chest pain, anemia, jaundice, liver disorders, inflammation, bronchitis, rheumatism, urinary tract infections, pain, bone fractures, toothache, gonorrhea, diabetes, cancer and other chronic diseases.

AIM OF THE REVIEW: This review aims to present up-to-date information regarding the taxonomy, botany, distribution, ethnomedicinal uses, phytochemistry, pharmacology and toxicological profile of G. pentaphylla. The presented information was analyzed critically to understand current work undertaken on this species and explore possible future prospects for this plant in pharmaceutical research.

MATERIALS & METHODS: Bibliographic databases, including Google Scholar, PubMed, Web of Science, ScienceDirect, SpringerLink, Wiley Online Library, Semantic Scholar, Europe PMC, Scopus, and MEDLINE, were explored thoroughly for the collection of relevant information. The structures of phytoconstituents were confirmed with PubChem and SciFinder databases. Taxonomical information on the plant was presented in accordance with The Plant List (version 1.1).

RESULTS: Extensive phytochemical investigations into different parts of G. pentaphylla have revealed the presence of at least 354 secondary metabolites belonging to structurally diverse classes including alkaloids, amides, phenolic compounds, flavonoids, glycosides, aromatic compounds, steroids, terpenoids, and fatty derivatives. A large number of in vitro and in vivo experiments have demonstrated that G. pentaphylla had anticancer, antimutagenic, antibacterial, antifungal, anthelmintic, mosquitocidal, antidiabetic, antihyperlipidemic, anti-oxidant, anti-inflammatory, analgesic, antipyretic, anti-arsenicosis, and wound healing properties. Toxicological studies have established the absence of any significant adverse reactions and showed that the plant had a moderate safety profile.

CONCLUSIONS: G. pentaphylla can be suggested as a source of inspiration for the development of novel drugs, especially anticancer, antimicrobial, anthelmintic, and mosquitocidal agents. Moreover, bioassay-guided investigations into its diverse classes of secondary metabolites, especially the large pool of nitrogen-containing alkaloids and amides, promises the development of novel drug candidates. Future pharmacological studies into this species are also warranted as many of its traditional uses are yet to be validated scientifically.

PMID:34116186 | DOI:10.1016/j.jep.2021.114313

J Med Internet Res. 2021 Jun 8;23(6):e25968. doi: 10.2196/25968.

ABSTRACT

BACKGROUND: Caregivers of people with dementia find it extremely difficult to choose the best care method because of complex environments and the variable symptoms of dementia. To alleviate this care burden, interventions have been proposed that use computer- or web-based applications. For example, an automatic diagnosis of the condition can improve the well-being of both the person with dementia and the caregiver. Other interventions support the individual with dementia in living independently.

OBJECTIVE: The aim of this study was to develop an ontology-based care knowledge management system for people with dementia that will provide caregivers with a care guide suited to the environment and to the individual patient's symptoms. This should also enable knowledge sharing among caregivers.

METHODS: To build the care knowledge model, we reviewed existing ontologies that contain concepts and knowledge descriptions relating to the care of those with dementia, and we considered dementia care manuals. The basic concepts of the care ontology were confirmed by experts in Korea. To infer the different care methods required for the individual dementia patient, the reasoning rules as defined in Semantic Web Rule Languages and Prolog were utilized. The accuracy of the care knowledge in the ontological model and the usability of the proposed system were evaluated by using the Pellet reasoner and OntOlogy Pitfall Scanner!, and a survey and interviews were conducted with caregivers working in care centers in Korea.

RESULTS: The care knowledge model contains six top-level concepts: care knowledge, task, assessment, person, environment, and medical knowledge. Based on this ontological model of dementia care, caregivers at a dementia care facility in Korea were able to access the care knowledge easily through a graphical user interface. The evaluation by the care experts showed that the system contained accurate care knowledge and a level of assessment comparable to normal assessment tools.

CONCLUSIONS: In this study, we developed a care knowledge system that can provide caregivers with care guides suited to individuals with dementia. We anticipate that the system could reduce the workload of caregivers.

PMID:34100762 | DOI:10.2196/25968

BMJ Open. 2021 May 31;11(6):e047040. doi: 10.1136/bmjopen-2020-047040.

ABSTRACT

OBJECTIVES: We compared intrapartum interventions and outcomes for mothers, neonates and children up to 16 years, for induction of labour (IOL) versus spontaneous labour onset in uncomplicated term pregnancies with live births.

DESIGN: We used population linked data from New South Wales, Australia (2001-2016) for healthy women giving birth at 37+0 to 41+6 weeks. Descriptive statistics and logistic regression were performed for intrapartum interventions, postnatal maternal and neonatal outcomes, and long-term child outcomes adjusted for maternal age, country of birth, socioeconomic status, parity and gestational age.

RESULTS: Of 474 652 included births, 69 397 (15%) had an IOL for non-medical reasons. Primiparous women with IOL versus spontaneous onset differed significantly for: spontaneous vaginal birth (42.7% vs 62.3%), instrumental birth (28.0% vs 23.9%%), intrapartum caesarean section (29.3% vs 13.8%), epidural (71.0% vs 41.3%), episiotomy (41.2% vs 30.5%) and postpartum haemorrhage (2.4% vs 1.5%). There was a similar trend in outcomes for multiparous women, except for caesarean section which was lower (5.3% vs 6.2%). For both groups, third and fourth degree perineal tears were lower overall in the IOL group: primiparous women (4.2% vs 4.9%), multiparous women (0.7% vs 1.2%), though overall vaginal repair was higher (89.3% vs 84.3%). Following induction, incidences of neonatal birth trauma, resuscitation and respiratory disorders were higher, as were admissions to hospital for infections (ear, nose, throat, respiratory and sepsis) up to 16 years. There was no difference in hospitalisation for asthma or eczema, or for neonatal death (0.06% vs 0.08%), or in total deaths up to 16 years.

CONCLUSION: IOL for non-medical reasons was associated with higher birth interventions, particularly in primiparous women, and more adverse maternal, neonatal and child outcomes for most variables assessed. The size of effect varied by parity and gestational age, making these important considerations when informing women about the risks and benefits of IOL.

PMID:34059509 | PMC:PMC8169493 | DOI:10.1136/bmjopen-2020-047040

Stud Health Technol Inform. 2021 May 24;278:94-100. doi: 10.3233/SHTI210056.

ABSTRACT

Metadata repositories are an indispensable component of data integration infrastructures and support semantic interoperability between knowledge organization systems. Standards for metadata representation like the ISO/IEC 11179 as well as the Resource Description Framework (RDF) and the Simple Knowledge Organization System (SKOS) by the World Wide Web Consortium were published to ensure metadata interoperability, maintainability and sustainability. The FAIR guidelines were composed to explicate those aspects in four principles divided in fifteen sub-principles. The ISO/IEC 21526 standard extends the 11179 standard for the domain of health care and mandates that SKOS be used for certain scenarios. In medical informatics, the composition of health care SKOS classification schemes is often managed by documentalists and data scientists. They use editors, which support them in producing comprehensive and valid metadata. Current metadata editors either do not properly support the SKOS resource annotations, require server applications or make use of additional databases for metadata storage. These characteristics are contrary to the application independency and versatility of raw Unicode SKOS files, e.g. the custom text arrangement, extensibility or copy & paste editing. We provide an application that adds navigation, auto completion and validity check capabilities on top of a regular Unicode text editor.

PMID:34042881 | DOI:10.3233/SHTI210056

Stud Health Technol Inform. 2021 May 27;281:1019-1020. doi: 10.3233/SHTI210335.

ABSTRACT

Catalogues of learning objectives for Biomedical and Health Informatics are relevant prerequisites for systematic and effective qualification. Catalogue management needs to integrate different catalogues and support collaborative revisioning. The Health Informatics Learning Objectives Navigator (HI-LONa) offers an open, interoperable platform based on Semantic Web Technology. At present HI-LONa contains 983 learning objectives of three relevant catalogues. HI-LONa successfully supported a multiprofessional consensus process.

PMID:34042830 | DOI:10.3233/SHTI210335

Stud Health Technol Inform. 2021 May 27;281:78-82. doi: 10.3233/SHTI210124.

ABSTRACT

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

PMID:34042709 | DOI:10.3233/SHTI210124

Stud Health Technol Inform. 2021 May 27;281:729-733. doi: 10.3233/SHTI210268.

ABSTRACT

Cognitive Behavioural Therapy (CBT) is an action-oriented psychotherapy that combines cognitive and behavioural techniques for psychosocial treatment for depression, and is considered by many to be the golden standard in psychotherapy. More recently, computerized CBT (CCBT) has been deployed to help increase availability and access to this evidence-based therapy. In this vein, a CBT ontology, as a shared common understanding of the domain, can facilitate the aggregation, verification, and operationalization of computerized CBT knowledge. Moreover, as opposed to black-box applications, ontology-enabled systems allow recommended, evidence-based treatment interventions to be traced back to the corresponding psychological concepts. We used a Knowledge Management approach to synthesize and computerize CBT knowledge from multiple sources into a CBT ontology, which allows generating personalized action plans for treating mild depression, using the Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We performed a formative evaluation of the CBT ontology in terms of its completeness, consistency, and conciseness.

PMID:34042672 | DOI:10.3233/SHTI210268

JMIR Med Inform. 2021 May 25;9(5):e23586. doi: 10.2196/23586.

ABSTRACT

BACKGROUND: Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.

OBJECTIVE: This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.

METHODS: We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic's electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.

RESULTS: With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.

CONCLUSIONS: Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

PMID:34032581 | DOI:10.2196/23586

Nucleic Acids Res. 2021 May 21:gkab373. doi: 10.1093/nar/gkab373. Online ahead of print.

ABSTRACT

Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic Web technologies. DeepGOWeb provides accurate and fast predictions and ensures that predicted functions are consistent with the Gene Ontology; it can provide predictions for any protein and any function in Gene Ontology. DeepGOWeb is freely available at https://deepgo.cbrc.kaust.edu.sa/.

PMID:34019664 | DOI:10.1093/nar/gkab373

Int J Life Cycle Assess. 2021;26(3):483-496. doi: 10.1007/s11367-021-01870-9.

ABSTRACT

INTRODUCTION: The flexibility of life cycle inventory (LCI) background data selection is increasing with the increasing availability of data, but this comes along with the challenge of using the background data with primary life cycle inventory data. To relieve the burden on the practitioner to create the linkages and reduce bias, this study aimed at applying automation to create foreground LCI from primary data and link it to background data to construct product system models (PSM).

METHODS: Three experienced LCA software developers were commissioned to independently develop software prototypes to address the problem of how to generate an operable PSM from a complex product specification. The participants were given a confidential product specification in the form of a Bill of Materials (BOM) and were asked to develop and test prototype software under a limited time period that converted the BOM into a foreground model and linked it with one or more a background datasets, along with a list of other functional requirements. The resulting prototypes were compared and tested with additional product specifications.

RESULTS: Each developer took a distinct approach to the problem. One approach used semantic similarity relations to identify best-fit background datasets. Another approach focused on producing a flexible description of the model structure that removed redundancy and permitted aggregation. Another approach provided an interactive web application for matching product components to standardized product classification systems to facilitate characterization and linking.

DISCUSSION: Four distinct steps were identified in the broader problem of automating PSM construction: creating a foreground model from product data, determining the quantitative properties of foreground model flows, linking flows to background datasets, and expressing the linked model in a format that could be used by existing LCA software. The three prototypes are complementary in that they address different steps and demonstrate alternative approaches. Manual work was still required in each case, especially in the descriptions of the product flows that must be provided by background datasets.

CONCLUSION: The study demonstrates the utility of a distributed, comparative software development, as applied to the problem of LCA software. The results demonstrate that the problem of automated PSM construction is tractable. The prototypes created advance the state of the art for LCA software.

PMID:34017158 | PMC:PMC8128697 | DOI:10.1007/s11367-021-01870-9

PLoS One. 2021 May 20;16(5):e0251899. doi: 10.1371/journal.pone.0251899. eCollection 2021.

ABSTRACT

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics' average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors' regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation's efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

PMID:34014987 | PMC:PMC8136850 | DOI:10.1371/journal.pone.0251899

Artif Intell Med. 2021 May;115:102074. doi: 10.1016/j.artmed.2021.102074. Epub 2021 Apr 15.

ABSTRACT

Clinical trials are the basis of Evidence-Based Medicine. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is published. Moreover, the independence of experts may be difficult to appraise. On the contrary, in many other domains, including medical risk analysis, the advent of data science, big data and visual analytics allowed moving from expert-based to fact-based knowledge. Since 12 years, many trial results are publicly available online in trial registries. Nevertheless, data science methods have not yet been applied widely to trial data. In this paper, we present a platform for analyzing the safety events reported during clinical trials and published in trial registries. This platform is based on an ontological model including 582 trials on pain treatments, and uses semantic web technologies for querying this dataset at various levels of granularity. It also relies on a 26-dimensional flower glyph for the visualization of the Adverse Drug Events (ADE) rates in 13 categories and 2 levels of seriousness. We illustrate the interest of this platform through several use cases and we were able to find back conclusions that were initially found during meta-analyses. The platform was presented to four experts in drug safety, and is publicly available online, with the ontology of pain treatment ADE.

PMID:34001324 | DOI:10.1016/j.artmed.2021.102074

BMJ Open. 2021 May 11;11(5):e040600. doi: 10.1136/bmjopen-2020-040600.

ABSTRACT

OBJECTIVE: To retrospectively assess a cohort of mothers for characteristics of injuries that they have suffered as a result of family and domestic violence (FDV) and which have required admission to a hospital during both the intrapartum and postpartum periods.

DESIGN AND SETTING: Retrospective, whole-population linked data study of FDV in Western Australia using the Western Australia birth registry from 1990 to 2009 and Hospital Morbidity Data System records from 1970 to 2013.

MAIN OUTCOME MEASURES: Number of hospitalisations, and mode, location and type of injuries recorded, with particular focus on the head and neck area.

RESULTS: There were 11 546 hospitalisations for mothers due to FDV. 8193 hospitalisations recorded an injury code to the head and/or neck region. The upper and middle thirds of the face and scalp were areas most likely to receive superficial injuries (58.7% or 4158 admissions), followed by the mouth and oral cavity (9.7% or 687 admissions). Fracture to the mandible accounted for 479 (4.2%) admissions and was almost equal to the sum of the next three most common facial fractures (nasal, maxillary and orbital floor). Mothers more likely to be hospitalised due to a head injury from FDV included those with more than one child (OR=1.17, 95% CI 1.03 to 1.30) and those with infants (<1 year old) (OR=1.40, 95% CI 1.04 to 1.90) and young children (<7 years old) (OR=1.15, 95% CI 1.01 to 1.30).

CONCLUSIONS: FDV is a serious and ongoing problem and front-line clinicians are in need of evidence-based guidelines to recognise and assist victims of FDV. Mothers with children in their care are a particularly vulnerable group.

PMID:33975864 | DOI:10.1136/bmjopen-2020-040600

Stud Health Technol Inform. 2021 May 7;279:144-146. doi: 10.3233/SHTI210101.

ABSTRACT

BACKGROUND: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles.

OBJECTIVE: To design a solution to improve FAIR for machines for the EJP RD API specification.

METHODS: A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source.

RESULTS: A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP.

CONCLUSION: grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

PMID:33965931 | DOI:10.3233/SHTI210101

Age Ageing. 2021 Sep 11;50(5):1482-1492. doi: 10.1093/ageing/afab099.

ABSTRACT

BACKGROUND: understanding care-home outbreaks of COVID-19 is a key public health priority in the ongoing pandemic to help protect vulnerable residents.

OBJECTIVE: to describe all outbreaks of COVID-19 infection in Scottish care-homes for older people between 01/03/2020 and 31/03/2020, with follow-up to 30/06/2020.

DESIGN AND SETTING: National linked data cohort analysis of Scottish care-homes for older people.

METHODS: data linkage was used to identify outbreaks of COVID-19 in care-homes. Care-home characteristics associated with the presence of an outbreak were examined using logistic regression. Size of outbreaks was modelled using negative binomial regression.

RESULTS: 334 (41%) Scottish care-homes for older people experienced an outbreak, with heterogeneity in outbreak size (1-63 cases; median = 6) and duration (1-94 days, median = 31.5 days). Four distinct patterns of outbreak were identified: 'typical' (38% of outbreaks, mean 11.2 cases and 48 days duration), severe (11%, mean 29.7 cases and 60 days), contained (37%, mean 3.5 cases and 13 days) and late-onset (14%, mean 5.4 cases and 17 days). Risk of a COVID-19 outbreak increased with increasing care-home size (for ≥90 beds vs <20, adjusted OR = 55.4, 95% CI 15.0-251.7) and rising community prevalence (OR = 1.2 [1.0-1.4] per 100 cases/100,000 population increase). No routinely available care-home characteristic was associated with outbreak size.

CONCLUSIONS: reducing community prevalence of COVID-19 infection is essential to protect those living in care-homes. More systematic national data collection to understand care-home residents and the homes in which they live is a priority in ensuring we can respond more effectively in future.

PMID:33963849 | PMC:PMC8136021 | DOI:10.1093/ageing/afab099

J Med Internet Res. 2021 May 6;23(5):e27918. doi: 10.2196/27918.

ABSTRACT

BACKGROUND: Despite decades of research to better understand suicide risk and to develop detection and prevention methods, suicide is still one of the leading causes of death globally. While large-scale studies using real-world evidence from electronic health records can identify who is at risk, they have not been successful at pinpointing when someone is at risk. Personalized social media and online search history data, by contrast, could provide an ongoing real-world datastream revealing internal thoughts and personal states of mind.

OBJECTIVE: We conducted this study to determine the feasibility and acceptability of using personalized online information-seeking behavior in the identification of risk for suicide attempts.

METHODS: This was a cohort survey study to assess attitudes of participants with a prior suicide attempt about using web search data for suicide prevention purposes, dates of lifetime suicide attempts, and an optional one-time download of their past web searches on Google. The study was conducted at the University of Washington School of Medicine Psychiatry Research Offices. The main outcomes were participants' opinions on internet search data for suicide prediction and intervention and any potential change in online information-seeking behavior proximal to a suicide attempt. Individualized nonparametric association analysis was used to assess the magnitude of difference in web search data features derived from time periods proximal (7, 15, 30, and 60 days) to the suicide attempts versus the typical (baseline) search behavior of participants.

RESULTS: A total of 62 participants who had attempted suicide in the past agreed to participate in the study. Internet search activity varied from person to person (median 2-24 searches per day). Changes in online search behavior proximal to suicide attempts were evident up to 60 days before attempt. For a subset of attempts (7/30, 23%) search features showed associations from 2 months to a week before the attempt. The top 3 search constructs associated with attempts were online searching patterns (9/30 attempts, 30%), semantic relatedness of search queries to suicide methods (7/30 attempts, 23%), and anger (7/30 attempts, 23%). Participants (40/59, 68%) indicated that use of this personalized web search data for prevention purposes was acceptable with noninvasive potential interventions such as connection to a real person (eg, friend, family member, or counselor); however, concerns were raised about detection accuracy, privacy, and the potential for overly invasive intervention.

CONCLUSIONS: Changes in online search behavior may be a useful and acceptable means of detecting suicide risk. Personalized analysis of online information-seeking behavior showed notable changes in search behavior and search terms that are tied to early warning signs of suicide and are evident 2 months to 7 days before a suicide attempt.

PMID:33955838 | DOI:10.2196/27918

Annu Rev Biomed Data Sci. 2020 Jul;3:23-41. doi: 10.1146/annurev-biodatasci-010820-091627. Epub 2020 Apr 7.

ABSTRACT

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.

PMID:33954284 | PMC:PMC8095730 | DOI:10.1146/annurev-biodatasci-010820-091627

J Obstet Gynaecol Res. 2021 Jun;47(6):1987-1996. doi: 10.1111/jog.14785. Epub 2021 May 1.

ABSTRACT

BACKGROUND: Despite the high prevalence of miscarriage, there are few studies which assess the concordance of a diagnosis of miscarriage in routinely collected health databases.

OBJECTIVES: To determine agreement and accuracy for the diagnosis of miscarriage between electronic health records (EHR), the Hospital Inpatient-Enquiry (HIPE) system, and hospital register books in Ireland.

METHODS: This is a retrospective study comparing agreement of diagnosis of miscarriage between three hospital data sources from January to June 2017. All inpatient admissions for miscarriage were reviewed from a single, tertiary maternity hospital in Ireland. Kappa, sensitivity, specificity, positive and negative predictive value were calculated.

RESULTS: In this retrospective concordance study, EHR records confirmed 96.2% diagnosis of miscarriage of HIPE records, and 95.1% of register books records. A total of 95 records were not recorded in the register books but were recorded in HIPE and EHR. This study found a considerable variability when comparing definitions of type of miscarriage (i.e., missed miscarriage, incomplete, and complete) between the three data sources.

CONCLUSION: Although this study found a high concordance in inpatient admissions for miscarriage between EHR, HIPE, and register books, a considerable discrepancy was found when classifying miscarriage between the three data sources.

PMID:33932071 | DOI:10.1111/jog.14785

Int J Mol Med. 2021 Jun;47(6):115. doi: 10.3892/ijmm.2021.4948. Epub 2021 Apr 28.

ABSTRACT

Demetra Application is a holistic integrated and scalable bioinformatics web‑based tool designed to assist medical experts and researchers in the process of diagnosing endometriosis. The application identifies the most prominent gene variants and single nucleotide polymorphisms (SNPs) causing endometriosis using the genomic data provided for the patient by a medical expert. The present study analyzed >28.000 endometriosis‑related publications using data mining and semantic techniques aimed towards extracting the endometriosis‑related genes and SNPs. The extracted knowledge was filtered, evaluated, annotated, classified, and stored in the Demetra Application Database (DAD). Moreover, an updated gene regulatory network with the genes implements in endometriosis was established. This was followed by the design and development of the Demetra Application, in which the generated datasets and results were included. The application was tested and presented herein with whole‑exome sequencing data from seven related patients with endometriosis. Endometriosis‑related SNPs and variants identified in genome‑wide association studies (GWAS), whole‑genome (WGS), whole‑exome (WES), or targeted sequencing information were classified, annotated and analyzed in a consolidated patient profile with clinical significance information. Probable genes associated with the patient's genomic profile were visualized using several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, in an effort to obtain a representative number of the most credible candidate genes and biological pathways associated with endometriosis. An evaluation analysis was performed on seven patients from a three‑generation family with endometriosis. All the recognized gene variants that were previously considered to be associated with endometriosis were properly identified in the output profile per patient, and by comparing the results, novel findings emerged. This novel and accessible webserver tool of endometriosis to assist medical experts in the clinical genomics and precision medicine procedure is available at http://geneticslab.aua.gr/.

PMID:33907838 | DOI:10.3892/ijmm.2021.4948