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

Neurol Ther. 2021 Apr 22. doi: 10.1007/s40120-021-00248-1. Online ahead of print.

ABSTRACT

INTRODUCTION: Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke.

METHODS: The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions.

RESULTS: The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users' views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence.

CONCLUSIONS: The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.

PMID:33886080 | DOI:10.1007/s40120-021-00248-1

Comput Biol Med. 2021 Apr 1;133:104360. doi: 10.1016/j.compbiomed.2021.104360. Online ahead of print.

ABSTRACT

Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

PMID:33836447 | DOI:10.1016/j.compbiomed.2021.104360

J Med Internet Res. 2021 Apr 9;23(4):e24656. doi: 10.2196/24656.

ABSTRACT

BACKGROUND: Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This "proof-of-concept" study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case.

OBJECTIVE: The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules.

METHODS: We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of "Semantic Sensor Network Ontology" and "Systematized Nomenclature of Medicine-Clinical Terms" to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based "Jena Framework" (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the "HermiT 1.4.3.x" ontology reasoner available in Protégé 5.x.

RESULTS: The proposed ontology has been implemented for the study case "obesity." However, it can be extended further to other lifestyle diseases. "UiA eHealth Ontology" has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with "Owl Viz," and the formal representation has been used to infer a participant's health status using the "HermiT" reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks.

CONCLUSIONS: This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

PMID:33835031 | DOI:10.2196/24656

One Health Outlook. 2020 Jun 8;2:11. doi: 10.1186/s42522-020-00017-4. eCollection 2020.

ABSTRACT

BACKGROUND: Health surveillance is an important element of disease prevention, control, and management. During the past two decades, there have been several initiatives to integrate health surveillance systems using various mechanisms ranging from the integration of data sources to changing organizational structures and responses. The need for integration is caused by an increasing demand for joint data collection, use and preparedness for emerging infectious diseases.

OBJECTIVE: To review the integration mechanisms in human and animal health surveillance systems and identify their contributions in strengthening surveillance systems attributes.

METHOD: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 checklist. Peer-reviewed articles were searched from PubMed, HINARI, Web of Science, Science Direct and advanced Google search engines. The review included articles published in English from 1900 to 2018. The study selection considered all articles that used quantitative, qualitative or mixed research methods. Eligible articles were assessed independently for quality by two authors using the QualSyst Tool and relevant information including year of publication, field, continent, addressed attributes and integration mechanism were extracted.

RESULTS: A total of 102 publications were identified and categorized into four pre-set integration mechanisms: interoperability (35), convergent integration (27), semantic consistency (21) and interconnectivity (19). Most integration mechanisms focused on sensitivity (44.1%), timeliness (41.2%), data quality (23.5%) and acceptability (17.6%) of the surveillance systems. Generally, the majority of the surveillance system integrations were centered on addressing infectious diseases and all hazards. The sensitivity of the integrated systems reported in these studies ranged from 63.9 to 100% (median = 79.6%, n = 16) and the rate of data quality improvement ranged from 73 to 95.4% (median = 87%, n = 4). The integrated systems were also shown improve timeliness where the recorded changes were reported to be ranging from 10 to 91% (median = 67.3%, n = 8).

CONCLUSION: Interoperability and semantic consistency are the common integration mechanisms in human and animal health surveillance systems. Surveillance system integration is a relatively new concept but has already been shown to enhance surveillance performance. More studies are needed to gain information on further surveillance attributes.

PMID:33829132 | PMC:PMC7993536 | DOI:10.1186/s42522-020-00017-4

PeerJ Comput Sci. 2021 Mar 8;7:e387. doi: 10.7717/peerj-cs.387. eCollection 2021.

ABSTRACT

While the publication of Linked Data has become increasingly common, the process tends to be a relatively complicated and heavy-weight one. Linked Data is typically published by centralized entities in the form of larger dataset releases, which has the downside that there is a central bottleneck in the form of the organization or individual responsible for the releases. Moreover, certain kinds of data entries, in particular those with subjective or original content, currently do not fit into any existing dataset and are therefore more difficult to publish. To address these problems, we present here an approach to use nanopublications and a decentralized network of services to allow users to directly publish small Linked Data statements through a simple and user-friendly interface, called Nanobench, powered by semantic templates that are themselves published as nanopublications. The published nanopublications are cryptographically verifiable and can be queried through a redundant and decentralized network of services, based on the grlc API generator and a new quad extension of Triple Pattern Fragments. We show here that these two kinds of services are complementary and together allow us to query nanopublications in a reliable and efficient manner. We also show that Nanobench makes it indeed very easy for users to publish Linked Data statements, even for those who have no prior experience in Linked Data publishing.

PMID:33817033 | PMC:PMC7959648 | DOI:10.7717/peerj-cs.387

PeerJ Comput Sci. 2020 Sep 21;6:e281. doi: 10.7717/peerj-cs.281. eCollection 2020.

ABSTRACT

It is essential for the advancement of science that researchers share, reuse and reproduce each other's workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

PMID:33816932 | PMC:PMC7924452 | DOI:10.7717/peerj-cs.281

Am J Audiol. 2021 Mar 30:1-16. doi: 10.1044/2021_AJA-20-00146. Online ahead of print.

ABSTRACT

Purpose People with hearing impairment (HI) face numerous challenges that can be minimized with the use of hearing aids and cochlear implants. Despite technological advances in these assistive hearing devices, musical perception remains difficult for these people. Tests and protocols developed to assess the musical perception of this audience were the target of this systematic review, whose objective was to investigate how assessments of musical perception in people with HI are carried out. Method Searches for primary articles were carried out in the PubMed/MEDLINE, Scopus, Web of Science, Latin American and Caribbean Health Sciences Literature, and ASHAWire databases. Search results were managed using EndNote X9 software, and analysis was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement. Results The 16 cross-sectional included studies analyzed music perception data from people with HI compared to a control group of participants with normal hearing. Among these, four studies were selected to be included in a meta-analysis, performed with timbre and melody. Variability was observed in the tests and between the levels of auditory perception skills analyzed in relation to the components of music. With respect to the tests, sound stimuli generated by synthesizers were the most used stimuli; with the exception of timbre evaluation, the most frequent test environment was a booth with sound attenuation, and the average intensity for presenting sound stimuli was 70 dB SPL. The most evaluated sound component was pitch, followed by rhythm and timbre, with a pattern of responses based on adaptive and psychoacoustic methods. Conclusions The heterogeneity of the musical parameters and the auditory abilities evaluated by the tests is a fact that can compromise evidence found in this area of study. It is worth considering the quality of samples that were recorded with real musical instruments and digitized afterward, in comparison with synthesized samples that do not seem to accurately represent real instruments. The need to minimize semantic parallelism that involves the auditory skills and elements of music involved in the assessment of musical perception is highlighted.

PMID:33784174 | DOI:10.1044/2021_AJA-20-00146

J Biomed Inform. 2021 Mar 26:103755. doi: 10.1016/j.jbi.2021.103755. Online ahead of print.

ABSTRACT

Resource Description Framework (RDF) is one of the three standardized data formats in the HL7 Fast Healthcare Interoperability Resources (FHIR) specification and is being used by healthcare and research organizations to join FHIR and non-FHIR data. However, RDF previously had not been integrated into popular FHIR tooling packages, hindering the adoption of FHIR RDF in the semantic web and other communities. The objective of the study is to develop and evaluate a Java based FHIR RDF data transformation toolkit to facilitate the use and validation of FHIR RDF data. We extended the popular HAPI FHIR tooling to add RDF support, thus enabling FHIR data in XML or JSON to be transformed to or from RDF. We also developed an RDF Shape Expression (ShEx)-based validation framework to verify conformance of FHIR RDF data to the ShEx schemas provided in the FHIR specification for FHIR versions R4 and R5. The effectiveness of ShEx validation was demonstrated by testing it against 2693 FHIR R4 examples and 2197 FHIR R5 examples that are included in the FHIR specification. A total of 5 types of errors including missing properties, unknown element, missing resourceType, invalid attribute value, and unknown resource name in the R5 examples were revealed, demonstrating the value of the ShEx in the quality assurance of the evolving R5 development. This FHIR RDF data transformation and validation framework, based on HAPI and ShEx, is robust and ready for community use in adopting FHIR RDF, improving FHIR data quality, and evolving the FHIR specification.

PMID:33781919 | DOI:10.1016/j.jbi.2021.103755

Trans GIS. 2021 Feb;25(1):424-449. doi: 10.1111/tgis.12692. Epub 2020 Oct 26.

ABSTRACT

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo-analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo-analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo-analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.

PMID:33776542 | PMC:PMC7983927 | DOI:10.1111/tgis.12692

J Biomed Inform. 2021 Mar 24:103750. doi: 10.1016/j.jbi.2021.103750. Online ahead of print.

ABSTRACT

Clinical decision support systems are assisting physicians in providing care to patients. However, in the context of clinical pathway management such systems are rather limited as they only take the current state of the patient into account and ignore the possible evolvement of that state in the future. In the past decade, the availability of big data in the healthcare domain did open a new era for clinical decision support. Machine learning technologies are now widely used in the clinical domain, nevertheless, mostly as a tool for disease prediction. A tool that not only predicts future states, but also enables adaptive clinical pathway management based on these predictions is still in need. This paper introduces weighted state transition logic, a logic to model state changes based on actions planned in clinical pathways. Weighted state transition logic extends linear logic by taking weights - numerical values indicating the quality of an action or an entire clinical pathway - into account. It allows us to predict the future states of a patient and it enables adaptive clinical pathway management based on these predictions. We provide an implementation of weighted state transition logic using semantic web technologies, which makes it easy to integrate semantic data and rules as background knowledge. Executed by a semantic reasoner, it is possible to generate a clinical pathway towards a target state, as well as to detect potential conflicts in the future when multiple pathways are coexisting. The transitions from the current state to the predicted future state are traceable, which builds trust from human users on the generated pathway.

PMID:33774204 | DOI:10.1016/j.jbi.2021.103750

Front Pharmacol. 2021 Mar 8;11:608068. doi: 10.3389/fphar.2020.608068. eCollection 2020.

ABSTRACT

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

PMID:33762928 | PMC:PMC7982727 | DOI:10.3389/fphar.2020.608068

Breast Cancer Res Treat. 2021 Jul;188(2):547-560. doi: 10.1007/s10549-021-06170-2. Epub 2021 Mar 21.

ABSTRACT

INTRODUCTION: Reducing variations in cancer treatment and survival is a key aim of the NSW Cancer Plan. Variations in breast cancer treatment and survival in NSW by remoteness and socioeconomic status of residence were investigated to determine benchmarks. Reducing variations in cancer treatment and survival is a key aim of the NSW Cancer Plan. Variations in breast cancer treatment and survival in NSW by remoteness and socioeconomic status of residence were investigated to determine benchmarks.

METHODS: A retrospective cohort study used linked data for invasive breast cancers, diagnosed in May 2002 to December 2015 from the NSW Cancer Registry, with corresponding inpatient, and medical and pharmaceutical insurance data. Associations between treatment modalities, area socioeconomic status and residential remoteness were explored using logistic regression. Predictors of breast cancer survival were investigated using Kaplan-Meier product-limit estimates and multivariate competing risk regression.

RESULTS: Results indicated a high 5-year disease-specific survival in NSW of 90%. Crude survival was equivalent by residential remoteness and marginally lower in lower socioeconomic areas. Competing risk regression showed equivalent outcomes by area socioeconomic status, except for the least disadvantaged quintile, which showed a higher survival. Higher sub-hazard ratios for death occurred for women with breast cancer aged 70 + years, and more advanced stage. Adjusted analyses indicated more advanced stage in lower socioeconomic areas, with less breast reconstruction and radiotherapy, and marginally less hormone therapy for women from these areas. Conversely, among these women who had breast conserving surgery, there was higher use of chemotherapy. Remoteness of residence was associated in adjusted analyses with less radiotherapy and less immediate breast reconstruction. In these short term data, remoteness of residence was not associated with lower survival.

CONCLUSION: This study provides benchmarks for monitoring future variations in treatment and survival.

PMID:33748922 | DOI:10.1007/s10549-021-06170-2