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

Data Brief. 2021 Feb 24;35:106841. doi: 10.1016/j.dib.2021.106841. eCollection 2021 Apr.

ABSTRACT

This article presents data that are further analyzed and interpreted in "Shouting at Each Other into the Void: A Semantic Network Analysis of Vaccine Hesitance and Support in Online Discourse Regarding California Law SB277" [1]. This research modified snowball sampling, a technique usually used to generate chains of informants that illuminate the structure of social networks, to collect digital documents following a chain of web links and recommendations, thus illuminating the underlying social, technical, and linguistic structure of online discourse. The resulting documents were manually coded according to the attitude towards vaccines they represented and/or the position they took with regard to California Senate Bill 277, a vaccine mandate policy that banned all nonmedical exemptions from school immunization requirements. Each attitude category, as well as the dataset as a whole, was subjected to quantitative linguistic analysis to identify key words and phrases in the data according to the frequency with which they appeared. A combination of that technique and semantic network analysis were used to generate clusters of related words that could be used for qualitative and narrative analysis, as detailed in the companion paper. The data collection and analysis processes described here will be of use to researchers conducting mixed-method analysis of online discourse who want their data to reflect the potential information and digital resources available to individuals who attempt to inform themselves about a particular topic using Internet searches. The data presented here could be useful for anyone seeking deeper insight into the linguistic and narrative patterns surrounding online debates about vaccination, controversial government policies, or both.

PMID:33748356 | PMC:PMC7966830 | DOI:10.1016/j.dib.2021.106841

Comput Ind Eng. 2021 Mar 15:107236. doi: 10.1016/j.cie.2021.107236. Online ahead of print.

ABSTRACT

The human suffering from diseases caused by life-threatening viruses such as SARS, Ebola, and COVID-19 motivated many of us to study and discover the best means to harness the potential of data integration to assist clinical researchers to curb these viruses. Integrating patients data with clinical trials data is enormously promising as it provides a comprehensive knowledge base that accelerates the clinical research response-ability to tackle emerging infectious disease outbreaks. This work introduces EMR2vec, a platform that customises advanced NLP, machine learning and semantic web techniques to link potential patients to suitable clinical trials. Linking these two different but complementary datasets allows clinicians and researchers to compare patients to clinical research opportunities or to automatically select patients for personalized clinical care. The platform derives a 'bag of medical terms' (BoMT) from eligibility criteria by normalizing extracted entities through SNOMED-CT ontology. With the usage of BoMT, an ontological reasoning method is proposed to represent EMR and clinical trials in a vector space model. The platform presents a matching process that reduces vector dimensionality using a neural network, then applies orthogonality projection to measure the similarity between vectors. Finally, the proposed EMR2vec platform is evaluated with an extendable prototype based on Big data tools.

PMID:33746344 | PMC:PMC7959675 | DOI:10.1016/j.cie.2021.107236

J Biomed Inform. 2021 Mar 15:103733. doi: 10.1016/j.jbi.2021.103733. Online ahead of print.

ABSTRACT

The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes.

PMID:33737205 | DOI:10.1016/j.jbi.2021.103733

PLoS One. 2021 Mar 18;16(3):e0248484. doi: 10.1371/journal.pone.0248484. eCollection 2021.

ABSTRACT

INTRODUCTION: People with ulnar, radial or median nerve injuries can present significant impairment of their sensory and motor functions. The prescribed treatment for these conditions often includes electrophysical therapies, whose effectiveness in improving symptoms and function is a source of debate. Therefore, this systematic review aims to provide an integrative overview of the efficacy of these modalities in sensorimotor rehabilitation compared to placebo, manual therapy, or between them.

METHODS: We conducted a systematic review according to PRISMA guidelines. We perform a literature review in the following databases: Biomed Central, Ebscohost, Lilacs, Ovid, PEDro, Sage, Scopus, Science Direct, Semantic Scholar, Taylor & Francis, and Web of Science, for the period 1980-2020. We include studies that discussed the sensorimotor rehabilitation of people with non-degenerative ulnar, radial, or median nerve injury. We assessed the quality of the included studies using the Risk of Bias Tool described in the Cochrane Handbook of Systematic Reviews of Interventions and the risk of bias across studies with the GRADE approach described in the GRADE Handbook.

RESULTS: Thirty-eight studies were included in the systematic review and 34 in the meta-analysis. The overall quality of evidence was rated as low or very low according to GRADE criteria. Low-level laser therapy and ultrasound showed favourable results in improving symptom severity and functional status compared to manual therapy. In addition, the low level laser showed improvements in pinch strength compared to placebo and pain (VAS) compared to manual therapy. Splints showed superior results to electrophysical modalities. The clinical significance of the results was assessed by effect size estimation and comparison with the minimum clinically important difference (MCID).

CONCLUSIONS: We found favourable results in pain relief, improvement of symptoms, functional status, and neurophysiological parameters for some electrophysical modalities, mainly when applied with a splint. Our results coincide with those obtained in some meta-analyses. However, none of these can be considered clinically significant.

TRIAL REGISTRATION: PROSPERO registration number CRD42020168792; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=168792.

PMID:33735212 | DOI:10.1371/journal.pone.0248484

Front Artif Intell. 2021 Feb 23;3:621577. doi: 10.3389/frai.2020.621577. eCollection 2020.

ABSTRACT

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

PMID:33733227 | PMC:PMC7940824 | DOI:10.3389/frai.2020.621577

JMIR Form Res. 2021 Mar 16;5(3):e24738. doi: 10.2196/24738.

ABSTRACT

BACKGROUND: Traditionally, digital health data management has been based on electronic health record (EHR) systems and has been handled primarily by centralized health providers. New mechanisms are needed to give patients more control over their digital health data. Personal health libraries (PHLs) provide a single point of secure access to patients' digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients' health by understanding medical events in the context of their lives.

OBJECTIVE: This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients' PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults.

METHODS: We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations.

RESULTS: To showcase the main features of the mobile health app and the PHL, we mapped those features onto a framework comprising the user requirements identified in a use case scenario that features a preventive intervention from the diabetes self-management domain. Ongoing development of the app requires a formative evaluation study and a clinical trial to assess the impact of the digital intervention on patient-users. We provide synopses of both study protocols.

CONCLUSIONS: The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge. By exposing the PHL functionality as an open service, we foster the development of third-party applications or services and provide motivational technological support in several projects crossing different domains of interest.

PMID:33724197 | DOI:10.2196/24738

J Korean Acad Nurs. 2021 Feb;51(1):68-79. doi: 10.4040/jkan.20213.

ABSTRACT

PURPOSE: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies.

METHODS: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6.

RESULTS: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured.

CONCLUSION: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

PMID:33706332 | DOI:10.4040/jkan.20213

Comput Struct Biotechnol J. 2021 Jan 29;19:1081-1091. doi: 10.1016/j.csbj.2021.01.037. eCollection 2021.

ABSTRACT

Diet is one of the main sources of exposure to toxic chemicals with carcinogenic potential, some of which are generated during food processing, depending on the type of food (primarily meat, fish, bread and potatoes), cooking methods and temperature. Although demonstrated in animal models at high doses, an unequivocal link between dietary exposure to these compounds with disease has not been proven in humans. A major difficulty in assessing the actual intake of these toxic compounds is the lack of standardised and harmonised protocols for collecting and analysing dietary information. The intestinal microbiota (IM) has a great influence on health and is altered in some diseases such as colorectal cancer (CRC). Diet influences the composition and activity of the IM, and the net exposure to genotoxicity of potential dietary carcinogens in the gut depends on the interaction among these compounds, IM and diet. This review analyses critically the difficulties and challenges in the study of interactions among these three actors on the onset of CRC. Machine Learning (ML) of data obtained in subclinical and precancerous stages would help to establish risk thresholds for the intake of toxic compounds generated during food processing as related to diet and IM profiles, whereas Semantic Web could improve data accessibility and usability from different studies, as well as helping to elucidate novel interactions among those chemicals, IM and diet.

PMID:33680352 | PMC:PMC7892627 | DOI:10.1016/j.csbj.2021.01.037