Glycobiology. 2021 Feb 23:cwab013. doi: 10.1093/glycob/cwab013. Online ahead of print.

ABSTRACT

Recent years have seen great advances in the development of glycoproteomics protocols and methods resulting in a sustainable increase in the reporting proteins, their attached glycans and glycosylation sites. However, only very few of these reports find their way into databases or data repositories. One of the major reasons are the absence of digital standard to represent glycoproteins and the challenging annotations with glycans. Depending on the experimental method, such a standard must be able to represent glycans as complete structures or as compositions, store not just single glycans but also represent glycoforms on a specific glycosylation side, deal with partially missing site information if no site mapping was performed, and store abundances or ratios of glycans within a glycoform of a specific site. In order to support the above, we have developed the GlycoConjugate Ontology (GlycoCoO) as a standard semantic framework to describe and represent glycoproteomics data. GlycoCoO can be used to represent glycoproteomics data in triplestores and can serve as a basis for data exchange formats. The ontology, database providers and supporting documentation are available online (https://github.com/glycoinfo/GlycoCoO).

PMID:33677548 | DOI:10.1093/glycob/cwab013

J Proteome Res. 2021 Mar 4. doi: 10.1021/acs.jproteome.0c00926. Online ahead of print.

ABSTRACT

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.

PMID:33661648 | DOI:10.1021/acs.jproteome.0c00926

Int J Environ Res Public Health. 2021 Feb 26;18(5):2309. doi: 10.3390/ijerph18052309.

ABSTRACT

Infants need sufficient nutrients even during disasters. Only qualitative descriptive analysis has been reported regarding nutritional problems of mothers and children after the Kumamoto earthquake, and non-subjective analysis is required. This study examined issues concerning maternal and child health, food and nutrition after the Kumamoto earthquake using automatic computer quantitative analysis from focus group interviews (FGIs). Study participants (n = 13) consisted of dietitians in charge of nutrition assistance of infants in affected areas. The content of the interviews was converted into text, nouns were extracted, and co-occurrence network diagram analysis was performed. In the severely damaged area, there were hygienic problems not only in the acute phase but also in the mid-to-long-term phase. "Allergy" was extracted in the surrounding area in the acute and the mid-to-long-term phase, but not in the severely damaged area as the acute phase issue. In the surrounding area, problems have shifted to health and the quality of diet in the mid-to-long-term phase. This objective analysis suggested that dietary problems for mothers and children after disaster occurred also in the mid-to-long-term phase. It will be necessary to combine the overall trends obtained in this study with the results of qualitative descriptive analysis.

PMID:33652781 | PMC:PMC7956302 | DOI:10.3390/ijerph18052309

Cognition. 2021 Jun;211:104631. doi: 10.1016/j.cognition.2021.104631. Epub 2021 Feb 24.

ABSTRACT

Older adults tend to have a broader vocabulary compared to younger adults - indicating a richer storage of semantic knowledge - but their retrieval abilities decline with age. Recent advances in quantitative methods based on network science have investigated the effect of aging on semantic memory structure. However, it is yet to be determined how this aging effect on semantic memory structure relates to its overall flexibility. Percolation analysis provides a quantitative measure of the flexibility of a semantic network, by examining how a semantic memory network is resistant to "attacks" or breaking apart. In this study, we incorporated percolation analyses to examine how semantic networks of younger and older adults break apart to investigate potential age-related differences in language production. We applied the percolation analysis to 3 independent sets of data (total N = 78 younger, 78 older adults) from which we generated semantic networks based on verbal fluency performance. Across all 3 datasets, the percolation integrals of the younger adults were larger than older adults, indicating that older adults' semantic networks were less flexible and broke down faster than the younger adults'. Our findings provide quantitative evidence for diminished flexibility in older adults' semantic networks, despite the stability of semantic knowledge across the lifespan. This may be one contributing factor to age-related differences in language production.

PMID:33639378 | PMC:PMC8058279 | DOI:10.1016/j.cognition.2021.104631

BMJ. 2021 Feb 25;372:n560. doi: 10.1136/bmj.n560.

NO ABSTRACT

PMID:33632849 | DOI:10.1136/bmj.n560

PLoS One. 2021 Feb 26;16(2):e0245847. doi: 10.1371/journal.pone.0245847. eCollection 2021.

ABSTRACT

Internet of Things (IoT) is the growing invention in the current development of different domains like industries, e-health, and education, etc. Semantic web of things (SWoT) is an extension of IoT that enhance the communication by behaving intelligently. SWoT comprises 7 layered architecture. The perception layer is an important layer for collecting data from devices and to communicate with its associated layer. The data loss at the perception layer is very common due to inadequate resources, unpredictable link, noise, collision, and unexpected damage. To address this problem, we propose a method based on Compressive Sensing which recovers and estimates sensory data from a low-rank structure. The contribution of this paper is three folds. Firstly, we determine the problem of data acquisition and data loss at semantic sensory nodes in SWoT. Secondly, we introduce a compressive sensing based framework for SWoT that recovers the data accurately using low-rank features. Thirdly, the data estimation method is utilized to reduce the volume of the data. Proposed Compressive Sensing based Data Recoverability and Estimation (CS-RE) method is evaluated and compared with the existing reconstruction methods. The simulation results on real sensory datasets depict that the proposed method significantly outperforms existing methods in terms of error ratio and data recoverability accuracy.

PMID:33635878 | DOI:10.1371/journal.pone.0245847

API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.

Healthc Inform Res. 2021 Jan;27(1):39-47

Authors: Syed S, Syed M, Syeda HB, Garza M, Bennett W, Bona J, Begum S, Baghal A, Zozus M, Prior F

Abstract
OBJECTIVES: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparate systems must be aggregated for analysis. Study participant records from various sources are linked together and to patient records when possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programming interface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) to further de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employs a pseudonymization method based on the type of incoming research data.
METHODS: For images, pseudonymization of PIDs is done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headers and returns the pseudonymized identifiers. For non-imaging clinical research data, PIDs provided by study principal investigators (PIs) are pseudonymized using a nightly automated process. The pseudonymized PIDs (P-PIDs) along with other protected health information is further de-identified using POSDA.
RESULTS: A sample of 250 PIDs pseudonymized by O-CAPP were selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process were validated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymization by API request based on the provided PID and P-PID mappings.
CONCLUSIONS: We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.

PMID: 33611875 [PubMed]

Disparities in the hospital cost of cardiometabolic diseases among lesbian, gay, and bisexual Canadians: a population-based cohort study using linked data.

Can J Public Health. 2020 06;111(3):417-425

Authors: Gupta N, Sheng Z

Abstract
OBJECTIVES: Sexual identity has been recognized as a social determinant of health; however, evidence is limited on sexual minority status as a possible contributor to inequalities in cardiometabolic outcomes and the related hospital burden. This study aimed to investigate the association between sexual identity and hospital costs for cardiometabolic diseases among a cohort of Canadians using linked survey and administrative data.
METHODS: Data from the 2007-2011 Canadian Community Health Survey were linked to acute-care inpatient records from the 2005/2006-2012/2013 Discharge Abstract Database. Multiple linear regression was used to assess the association between self-reported sexual identity and inpatient resource use for cardiometabolic diseases.
RESULTS: Among the population ages 18-59, 2.1% (95% CI 1.9-2.2) identified as lesbian, gay, or bisexual (LGB). LGB individuals more often reported having diabetes or heart disease compared with heterosexuals. The mean inflation-adjusted cost for cardiometabolic-related hospitalizations was found to be significantly higher among LGB patients (CAD$26,702; 95% CI 26,166-60,365) than among their heterosexual counterparts ($10,137; 95% CI 8,639-11,635), in part a reflection of longer hospital stays (13.6 days versus 5.1 days). Inpatient costs remained 54% (95% CI 8-119) higher among LGB patients after controlling for socio-demographics, health status, and health behaviours.
CONCLUSION: This study revealed a disproportionate cost for potentially avoidable hospitalizations for cardiometabolic conditions among LGB patients, suggesting important unmet healthcare needs even in the Canadian context of universal coverage.

PMID: 32112310 [PubMed - indexed for MEDLINE]

Brain Behav. 2021 Apr;11(4):e02065. doi: 10.1002/brb3.2065. Epub 2021 Feb 18.

ABSTRACT

INTRODUCTION: The semantic network is an important mediator of language, enabling both speech production and the comprehension of multimodal stimuli. A major challenge in the field of neurosurgery is preventing semantic deficits. Multiple cortical areas have been linked to semantic processing, though knowledge of network connectivity has lacked anatomic specificity. Using attentional task-based fMRI studies, we built a neuroanatomical model of this network.

METHODS: One hundred and fifty-five task-based fMRI studies related to categorization of visual words and objects, and auditory words and stories were used to generate an activation likelihood estimation (ALE). Cortical parcellations overlapping the ALE were used to construct a preliminary model of the semantic network based on the cortical parcellation scheme previously published under the Human Connectome Project. Deterministic fiber tractography was performed on 25 randomly chosen subjects from the Human Connectome Project, to determine the connectivity of the cortical parcellations comprising the network.

RESULTS: The ALE analysis demonstrated fourteen left hemisphere cortical regions to be a part of the semantic network: 44, 45, 55b, IFJa, 8C, p32pr, SFL, SCEF, 8BM, STSdp, STSvp, TE1p, PHT, and PBelt. These regions showed consistent interconnections between parcellations. Notably, the anterior temporal pole, a region often implicated in semantic function, was absent from our model.

CONCLUSIONS: We describe a preliminary cortical model for the underlying structural connectivity of the semantic network. Future studies will further characterize the neurotractographic details of the semantic network in the context of medical application.

PMID:33599397 | PMC:PMC8035438 | DOI:10.1002/brb3.2065

A Novel Computerized Cognitive Stress Test to Detect Mild Cognitive Impairment.

J Prev Alzheimers Dis. 2021;8(2):135-141

Authors: Curiel Cid RE, Crocco EA, Kitaigorodsky M, Beaufils L, Peña PA, Grau G, Visser U, Loewenstein DA

Abstract
BACKGROUND: The Loewenstein Acevedo Scales of Semantic Interference and Learning (LASSI-L) is a novel and increasingly employed instrument that has outperformed widely used cognitive measures as an early correlate of elevated brain amyloid and neurodegeneration in prodromal Alzheimer's Disease (AD). The LASSI-L has distinguished those with amnestic mild cognitive impairment (aMCI) and high amyloid load from aMCI attributable to other non-AD conditions. The authors designed and implemented a web-based brief computerized version of the instrument, the LASSI-BC, to improve standardized administration, facilitate scoring accuracy, real-time data entry, and increase the accessibility of the measure.
OBJECTIVE: The psychometric properties and clinical utility of the brief computerized version of the LASSI-L was evaluated, together with its ability to differentiate older adults who are cognitively normal (CN) from those with amnestic Mild Cognitive Impairment (aMCI).
METHODS: After undergoing a comprehensive uniform clinical and neuropsychological evaluation using traditional measures, older adults were classified as cognitively normal or diagnosed with aMCI. All participants were administered the LASSI-BC, a computerized version of the LASSI-L. Test-retest and discriminant validity was assessed for each LASSI-BC subscale.
RESULTS: LASSI-BC subscales demonstrated high test-retest reliability, and discriminant validity was attained.
CONCLUSIONS: The LASSI-BC, a brief computerized version of the LASSI-L is a valid and useful cognitive tool for the detection of aMCI among older adults.

PMID: 33569559 [PubMed - in process]

Characterizing Tractability of Simple Well-Designed Pattern Trees with Projection.

Theory Comput Syst. 2021;65(1):3-41

Authors: Mengel S, Skritek S

Abstract
We study the complexity of evaluating well-designed pattern trees, a query language extending conjunctive queries with the possibility to define parts of the query to be optional. This possibility of optional parts is important for obtaining meaningful results over incomplete data sources as it is common in semantic web settings. Recently, a structural characterization of the classes of well-designed pattern trees that can be evaluated in polynomial time was shown. However, projection-a central feature of many query languages-was not considered in this study. We work towards closing this gap by giving a characterization of all tractable classes of simple well-designed pattern trees with projection (under some common complexity theoretic assumptions). Since well-designed pattern trees correspond to the fragment of well-designed {AND, OPTIONAL}-SPARQL queries this gives a complete description of the tractable classes of queries with projections in this fragment that can be characterized by the underlying graph structures of the queries. For non-simple pattern trees the tractability criteria for simple pattern trees do not capture all tractable classes. We thus extend the characterization for the non-simple case in order to capture some additional tractable cases.

PMID: 33568963 [PubMed]

Neural Netw. 2021 May;137:63-74. doi: 10.1016/j.neunet.2020.12.009. Epub 2021 Jan 22.

ABSTRACT

As deep neural net architectures minimize loss, they accumulate information in a hierarchy of learned representations that ultimately serve the network's final goal. Different architectures tackle this problem in slightly different ways, but all create intermediate representational spaces built to inform their final prediction. Here we show that very different neural networks trained on two very different tasks build knowledge representations that display similar underlying patterns. Namely, we show that the representational spaces of several distributional semantic models bear a remarkable resemblance to several Convolutional Neural Network (CNN) architectures (trained for image classification). We use this information to explore the network behavior of CNNs (1) in pretrained models, (2) during training, and (3) during adversarial attacks. We use these findings to motivate several applications aimed at improving future research on CNNs. Our work illustrates the power of using one model to explore another, gives new insights into the function of CNN models, and provides a framework for others to perform similar analyses when developing new architectures. We show that one neural network model can provide a window into understanding another.

PMID:33556802 | DOI:10.1016/j.neunet.2020.12.009

Pre-treatment graph measures of a functional semantic network are associated with naming therapy outcomes in chronic aphasia.

Brain Lang. 2020 08;207:104809

Authors: Johnson JP, Meier EL, Pan Y, Kiran S

Abstract
Naming treatment outcomes in post-stroke aphasia are variable and the factors underlying this variability are incompletely understood. In this study, 26 patients with chronic aphasia completed a semantic judgment fMRI task before receiving up to 12 weeks of naming treatment. Global (i.e., network-wide) and local (i.e., regional) graph theoretic measures of pre-treatment functional connectivity were analyzed to identify differences between patients who responded most and least favorably to treatment (i.e., responders and nonresponders) and determine if network measures predicted naming improvements. Responders had higher levels of global integration (i.e., average network strength and global efficiency) than nonresponders, and these measures predicted treatment effects after controlling for lesion volume and age. Group differences in local measures were identified in several regions associated with a variety of cognitive functions. These results suggest there is a meaningful and possibly prognostically-informative relationship between patients' functional network properties and their response to naming therapy.

PMID: 32505940 [PubMed - indexed for MEDLINE]

Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.

Brief Bioinform. 2021 Feb 04;:

Authors: Liu Y, Zhu YH, Song X, Song J, Yu DJ

Abstract
As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted features, which depict the characteristics of different protein folds; however, effective feature extraction methods still represent the bottleneck for further performance improvement of protein fold recognition. As a powerful feature extractor, deep convolutional neural network (DCNN) can automatically extract discriminative features for fold recognition without human intervention, which has demonstrated an impressive performance on protein fold recognition. Despite the encouraging progress, DCNN often acts as a black box, and as such, it is challenging for users to understand what really happens in DCNN and why it works well for protein fold recognition. In this study, we explore the intrinsic mechanism of DCNN and explain why it works for protein fold recognition using a visual explanation technique. More specifically, we first trained a VGGNet-based DCNN model, termed VGGNet-FE, which can extract fold-specific features from the predicted protein residue-residue contact map for protein fold recognition. Subsequently, based on the trained VGGNet-FE, we implemented a new contact-assisted predictor, termed VGGfold, for protein fold recognition; we then visualized what features were extracted by each of the convolutional layers in VGGNet-FE using a deconvolution technique. Furthermore, we visualized the high-level semantic information, termed fold-discriminative region, of a predicted contact map from the localization map obtained from the last convolutional layer of VGGNet-FE. It is visually confirmed that VGGNet-FE could effectively extract distinct fold-discriminative regions for different types of protein folds, thereby accounting for the improved performance of VGGfold for protein fold recognition. In summary, this study is of great significance for both understanding the working principle of DCNNs in protein fold recognition and exploring the relationship between the predicted protein contact map and protein tertiary structure. This proposed visualization method is flexible and applicable to address other DCNN-based bioinformatics and computational biology questions. The online web server of VGGfold is freely available at http://csbio.njust.edu.cn/bioinf/vggfold/.

PMID: 33537753 [PubMed - as supplied by publisher]

Seasonality of Back Pain in Italy: An Infodemiology Study.

Int J Environ Res Public Health. 2021 Feb 01;18(3):

Authors: Ciaffi J, Meliconi R, Landini MP, Mancarella L, Brusi V, Faldini C, Ursini F

Abstract
BACKGROUND: E-health tools have been used to assess the temporal variations of different health problems. The aim of our infodemiology study was to investigate the seasonal pattern of search volumes for back pain in Italy.
METHODS: In Italian, back pain is indicated by the medical word "lombalgia". Using Google Trends, we selected the three search terms related to "lombalgia" with higher relative search volumes (RSV), (namely, "mal di schiena", "dolore alla schiena" and "dolore lombare"), representing the semantic preferences of users when performing web queries for back pain in Italy. Wikipedia page view statistics were used to identify the number of visits to the page "lombalgia". Strength and direction of secular trends were assessed using the Mann-Kendall test. Cosinor analysis was used to evaluate the potential seasonality of back pain-related RSV.
RESULTS: We found a significant upward secular trend from 2005 to 2020 for search terms "mal di schiena" (τ = 0.734, p < 0.0001), "dolore alla schiena" (τ = 0.713, p < 0.0001) and "dolore lombare" (τ = 0.628, p < 0.0001). Cosinor analysis on Google Trends RSV showed a significant seasonality for the terms "mal di schiena" (pcos < 0.001), "dolore alla schiena" (pcos < 0.0001), "dolore lombare" (pcos < 0.0001) and "lombalgia" (pcos = 0.017). Cosinor analysis performed on views for the page "lombalgia" in Wikipedia confirmed a significant seasonality (pcos < 0.0001). Both analyses demonstrated a peak of interest in winter months and decrease in spring/summer.
CONCLUSIONS: Our infodemiology approach revealed significant seasonal fluctuations in search queries for back pain in Italy, with peaking volumes during the coldest months of the year.

PMID: 33535709 [PubMed - in process]

Applications of weighted association networks applied to compositional data in biology.

Environ Microbiol. 2020 08;22(8):3020-3038

Authors: Espinoza JL, Shah N, Singh S, Nelson KE, Dupont CL

Abstract
Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

PMID: 32436334 [PubMed - indexed for MEDLINE]

biotoolsSchema: a formalized schema for bioinformatics software description.

Gigascience. 2021 Jan 27;10(1):

Authors: Ison J, Ienasescu H, Rydza E, Chmura P, Rapacki K, Gaignard A, Schwämmle V, van Helden J, Kalaš M, Ménager H

Abstract
BACKGROUND: Life scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description-and cataloguing-of bioinformatics resources.
FINDINGS: Here we describe biotoolsSchema, a formalized information model that balances the needs of conciseness for rapid adoption against the provision of rich technical information and scientific context. biotoolsSchema results from a series of community-driven workshops and is deployed in the bio.tools registry, providing the scientific community with >17,000 machine-readable and human-understandable descriptions of software and other digital life-science resources. We compare our approach to related initiatives and provide alignments to foster interoperability and reusability.
CONCLUSIONS: biotoolsSchema supports the formalized, rigorous, and consistent specification of the syntax and semantics of bioinformatics resources, and enables cataloguing efforts such as bio.tools that help scientists to find, comprehend, and compare resources. The use of biotoolsSchema in bio.tools promotes the FAIRness of research software, a key element of open and reproducible developments for data-intensive sciences.

PMID: 33506265 [PubMed - in process]

Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.

J Med Internet Res. 2021 Jan 26;23(1):e24594

Authors: Gaudet-Blavignac C, Foufi V, Bjelogrlic M, Lovis C

Abstract
BACKGROUND: Interoperability and secondary use of data is a challenge in health care. Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) has become the universal language of health care and presents characteristics of a natural language. Its use to represent clinical free text could constitute a solution to improve interoperability.
OBJECTIVE: Although the use of SNOMED and SNOMED CT has already been reviewed, its specific use in processing and representing unstructured data such as clinical free text has not. This review aims to better understand SNOMED CT's use for representing free text in medicine.
METHODS: A scoping review was performed on the topic by searching MEDLINE, Embase, and Web of Science for publications featuring free-text processing and SNOMED CT. A recursive reference review was conducted to broaden the scope of research. The review covered the type of processed data, the targeted language, the goal of the terminology binding, the method used and, when appropriate, the specific software used.
RESULTS: In total, 76 publications were selected for an extensive study. The language targeted by publications was 91% (n=69) English. The most frequent types of documents for which the terminology was used are complementary exam reports (n=18, 24%) and narrative notes (n=16, 21%). Mapping to SNOMED CT was the final goal of the research in 21% (n=16) of publications and a part of the final goal in 33% (n=25). The main objectives of mapping are information extraction (n=44, 39%), feature in a classification task (n=26, 23%), and data normalization (n=23, 20%). The method used was rule-based in 70% (n=53) of publications, hybrid in 11% (n=8), and machine learning in 5% (n=4). In total, 12 different software packages were used to map text to SNOMED CT concepts, the most frequent being Medtex, Mayo Clinic Vocabulary Server, and Medical Text Extraction Reasoning and Mapping System. Full terminology was used in 64% (n=49) of publications, whereas only a subset was used in 30% (n=23) of publications. Postcoordination was proposed in 17% (n=13) of publications, and only 5% (n=4) of publications specifically mentioned the use of the compositional grammar.
CONCLUSIONS: SNOMED CT has been largely used to represent free-text data, most frequently with rule-based approaches, in English. However, currently, there is no easy solution for mapping free text to this terminology and to perform automatic postcoordination. Most solutions conceive SNOMED CT as a simple terminology rather than as a compositional bag of ontologies. Since 2012, the number of publications on this subject per year has decreased. However, the need for formal semantic representation of free text in health care is high, and automatic encoding into a compositional ontology could be a solution.

PMID: 33496673 [PubMed - as supplied by publisher]

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey.

J Healthc Eng. 2020;2020:8865808

Authors: Hammad R, Barhoush M, Abed-Alguni BH

Abstract
Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

PMID: 33489061 [PubMed - in process]

Small Semantic Networks in Individuals with Autism Spectrum Disorder Without Intellectual Impairment: A Verbal Fluency Approach.

J Autism Dev Disord. 2020 Nov;50(11):3967-3987

Authors: Ehlen F, Roepke S, Klostermann F, Baskow I, Geise P, Belica C, Tiedt HO, Behnia B

Abstract
Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored differences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fluency tasks ('animals', 'human feature', 'verbs', 'r-words') via curve fitting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n = 39), participants with ASD (n = 32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without affecting strength of associations or content.

PMID: 32198662 [PubMed - indexed for MEDLINE]