J Biomed Semantics. 2022 Jul 26;13(1):21. doi: 10.1186/s13326-022-00274-4.

ABSTRACT

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

PMID:35883181 | DOI:10.1186/s13326-022-00274-4

J Biomed Inform. 2022 Sep;133:104144. doi: 10.1016/j.jbi.2022.104144. Epub 2022 Jul 22.

ABSTRACT

Medical named entity recognition (MNER) is a fundamental component of understanding the unstructured medical texts in electronic health records, and it has received widespread attention in both academia and industry. However, the previous approaches of MNER do not make full use of hierarchical semantics from morphology to syntactic relationships like word dependency. Furthermore, extracting entities from Chinese medical texts is a more complex task because it usually contains for example homophones or pictophonetic characters. In this paper, we propose a multi-level semantic fusion network for Chinese medical named entity recognition, which fuses semantic information on morphology, character, word and syntactic level. We take radical as morphology semantic, pinyin and character dictionary as character semantic, word dictionary as word semantic, and these semantic features are fused by BiLSTM to get the contextualized representation. Then we use a graph neural network to model word dependency as syntactic semantic to enhance the contextualized representation. The experimental results show the effectiveness of the proposed model on two public datasets and robustness in real-world scenarios.

PMID:35878823 | DOI:10.1016/j.jbi.2022.104144

Curr Oncol. 2022 Jun 27;29(7):4541-4557. doi: 10.3390/curroncol29070359.

ABSTRACT

BACKGROUND: The burden of out-of-pocket costs among cancer patients/survivors in Canada is not well understood. The objective of this study was to examine the health-related out-of-pocket cost burden experienced by households with a cancer patient/survivor compared to those without, examine the components of health-related costs and determine who experiences a greater burden.

DATA AND METHODS: This study used a data linkage between the Survey of Household Spending and the Canadian Cancer Registry to identify households with a cancer patient/survivor (cases) and those without (controls). The out-of-pocket burden (out-of-pocket costs measured relative to household income) and mean costs were described and regression analyses examined the characteristics associated with the household out-of-pocket burden and annual out-of-pocket costs.

RESULTS: The health-related out-of-pocket cost burden and annual costs measured in households with a cancer patient/survivor were 3.08% (95% CI: 2.55-3.62%) and CAD 1600 (95% CI: 1456-1759), respectively, compared to a burden of 2.84% (95% CI: 2.31-3.38) and annual costs of CAD 1511 (95% CI: 1377-1659) measured in control households, respectively. Households with a colorectal cancer patient/survivor had a significantly higher out-of-pocket burden compared to controls (mean difference: 1.0%, 95% CI: 0.18, 0.46). Among both cases and controls, the lowest income quintile households experienced the highest health-related out-of-pocket cost burden.

INTERPRETATION: Within a universal health care system, it is still relevant to monitor health-related out-of-pocket spending that is not covered by existing insurance mechanisms; however, this is not routinely assessed in Canada. We demonstrate the feasibility of measuring such costs in households with a cancer patient/survivor using routinely collected data. While the burden and annual health-related out-of-pocket costs of households with a cancer patient/survivor were not significantly higher than control households in this study, the routine measurement of out-of-pocket costs in Canada could be systemized, providing a novel, system-level, equity-informed performance indicator, which is relevant for monitoring inequities in the burden of out-of-pocket costs.

PMID:35877219 | PMC:PMC9322389 | DOI:10.3390/curroncol29070359

Methods Inf Med. 2022 Jul 20. doi: 10.1055/a-1905-5639. Online ahead of print.

ABSTRACT

BACKGROUND: In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list.

OBJECTIVE: This study aimed to design and evaluate a novel practical web-based differential diagnosis generator solution in primary care.

METHODS: In this research, a new online clinical decision support system, called DxGenerator, was designed to improve diagnostic accuracy; to this end, an attempt was made to converge a semantic database with the unified medical language system (UMLS) knowledge base, using MetaMap tool and natural language processing (NLP). In this regard, 120 diseases of gastrointestinal organs causing abdominal pain were modeled into the database. After designing an inference engine and a pseudo-free-text interactive interface, 172 patient vignettes were inputted into DxGenerator and ISABEL, the most accurate similar system. The Wilcoxon signed ranked test was used to compare the position of correct diagnoses in DxGenerator and ISABEL. The alpha level was defined as 0.05.

RESULTS: On a total of 172 vignettes, the mean and standard deviation of correct diagnosis positions improved from 4.2±5.3 in ISABEL to 3.2±3.9 in DxGenerator. This improvement was significant in the subgroup of uncommon diseases (P-value < 0.05).

CONCLUSION: Using UMLS knowledge base and MetaMap Tools can improve the accuracy of diagnostic systems in which terms are entered in a free text manner. Applying these new methods will help the medical community accept medical diagnostic systems better.

PMID:35858654 | DOI:10.1055/a-1905-5639

Database (Oxford). 2022 Jul 15;2022:baac059. doi: 10.1093/database/baac059.

ABSTRACT

The use of probiotics to improve health via the modulation of gut microbiota has gained wide attention. The growing volume of investigations of probiotic microorganisms and commercialized probiotic products has created the need for a database to organize the health-promoting functions driven by probiotics reported in academic articles, clinical trials and patents. We constructed ProBioQuest to collect up-to-date literature related to probiotics from PubMed.gov, ClinicalTrials.gov and PatentsView. More than 2.8 million articles have been collected. Automated information technology-assisted procedures enabled us to collect the data continuously, providing the most up-to-date information. Statistical functions and semantic analyses are provided on the website as an advanced search engine, which contributes to the semantic tool of this database for information search and analyses. The semantic analytical output provides categorized search results and functions to enhance further analysis. A keyword bank is included which can display multiple tables of contents. Users can select keywords from different displayed categories to achieve easily filtered searches. Additional information on the searched items can be browsed via the link-out function. ProBioQuest is not only useful to scientists and health professionals but also to dietary supplement manufacturers and the general public. In this paper, the method we used to build this database-web system is described. Applications of ProBioQuest for several literature-based analyses of probiotics are included as examples of the various uses of this search engine. ProBioQuest can be accessed free of charge at http://kwanlab.bio.cuhk.edu.hk/PBQ/. Database URL: http://kwanlab.bio.cuhk.edu.hk/PBQ/.

PMID:35849028 | DOI:10.1093/database/baac059

J Commun Disord. 2022 Sep-Oct;99:106247. doi: 10.1016/j.jcomdis.2022.106247. Epub 2022 Jul 8.

ABSTRACT

PURPOSE: Kenett et al. (2013) report that the sematic networks, measured by using an oral semantic fluency task, of children with cochlear implants (CI) are less structured compared to the sematic networks of children with typical hearing (TH). This study aims to evaluate if such differences are only evident if children with CI are compared to children with TH matched on chronological age, or also if they are compared to children with TH matched on hearing age.

METHOD: The performance of a group of children with CI on a verbal fluency task was compared to the performance of a group of chronological-age matched children with TH. Subsequently, computational network analysis was used to compare the semantic network structure of the groups. The same procedure was applied to compare a group of children with CI to a group of hearing-age matched children with TH.

RESULTS: The children with CI perform on the same level on an oral semantic verbal fluency task as the children with TH matched on hearing age. There are significant differences in terms of the structure of the semantic network between the groups. The magnitude of these differences is very small and they are non-significant for a proportion of nodes included in the bootstrap analysis. This indicates that there is no true difference between the networks. Hearing age, but not age at implantation was found to be significantly positively correlated with semantic verbal fluency performance for the children with CI.

CONCLUSIONS: The results from the current study indicate that length of exposure to the tested language is an important factor for the structure of the semantic network and the performance on a semantic verbal fluency task for children with CI. Further studies are needed to explore the role of the accessibility of the language input for the development of semantic networks of children with CI.

PMID:35843069 | DOI:10.1016/j.jcomdis.2022.106247

J Acad Consult Liaison Psychiatry. 2022 Jul 12:S2667-2960(22)00294-4. doi: 10.1016/j.jaclp.2022.07.002. Online ahead of print.

ABSTRACT

INTRODUCTION: Since Kahlbaum's classic 19th-century description of catatonia our conceptualization of this syndrome, as well treatment options for it, have advanced considerably. However, little is known about the current state of the catatonia literature since a comprehensive bibliometric analysis of it has not yet been undertaken.

OBJECTIVE: The purpose of this study was to conduct a bibliometric analysis, along with a content analysis of articles reporting new findings, to better understand the catatonia literature and how catatonia research is changing.

METHODS: Using the search term "Title(catatoni*)" in Web of Science (WoS) Core Collection for all available years (1965-2020), all available publications (articles, proceeding papers, reviews) pertaining directly to catatonia were identified, and metadata extracted. Semantic and co-authorship network analyses were conducted. A content analysis was also conducted on all available case reports, case series, and research articles written in English.

RESULTS: 1,015 articles were identified representing 2,861 authors, 346 journals, and 15,639 references. Average publications per year over the last twenty years (31.3) more than doubled in comparison to the twenty years prior (12.8). The top three most common journals were Psychosomatics/Journal of the Academy of Consultation-Liaison Psychiatry, Journal of ECT, and Schizophrenia Research, which represented 12.6% of all publications. Content analysis revealed that catatonia articles are increasingly published in non-psychiatric journals. There was a notable paucity of clinical trials throughout the study period. Since 2003, articles on catatonia secondary to a general medical condition, as well as articles including child/adolescent patients and patients with autism spectrum disorder or intellectual disability, have made up increasing shares of the literature, with a smaller proportion of articles reporting periodic or recurrent catatonia. We noted decreased in the proportion of articles detailing animal/in vitro studies, genetic/heredity studies, and clinical trials, along with stagnation in the proportion of neuroimaging studies.

CONCLUSION: The catatonia literature is growing through contributions from authors and institutions across multiple countries. However, recent growth has largely been driven by increased case reports, with significant downturns observed in both clinical and basic science research articles. A dearth of clinical trials evaluating potential treatments remains a critical gap in the catatonia literature.

PMID:35840002 | DOI:10.1016/j.jaclp.2022.07.002

BMC Bioinformatics. 2022 Jul 14;23(Suppl 6):281. doi: 10.1186/s12859-022-04797-6.

ABSTRACT

BACKGROUND: Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS: Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of this ontology utilizes standard concepts and properties from OBO Foundry ontologies. Also, the software reasoner indicated no logical inconsistencies with the ontology. With sample model cards of machine learning models for bioinformatics research (HIV social networks and adverse outcome prediction for stent implantation), we showed the coverage and usefulness of our model in transforming static model card reports to a computable format for machine-based processing.

CONCLUSIONS: The benefit of our work is that it utilizes expansive and standard terminologies and scientific rigor promoted by biomedical ontologists, as well as, generating an avenue to make model cards machine-readable using semantic web technology. Our future goal is to assess the veracity of our model and later expand the model to include additional concepts to address terminological gaps. We discuss tools and software that will utilize our ontology for potential application services.

PMID:35836130 | DOI:10.1186/s12859-022-04797-6

Biosystems. 2022 Jul 9:104735. doi: 10.1016/j.biosystems.2022.104735. Online ahead of print.

ABSTRACT

The hypothesis presented here is that codes as described by Marcello Barbieri are the fundamental principle behind biological modularity. Modularity has been studied in different life science disciplines, especially in the fields of evolution and development, as well as in network biology, yet there is still no consensus on how modularity evolved itself. Modularity is basically the functional integrity of multiple molecular players involved in a common process. Codes as defined by Barbieri describe a tripartite relation involving an adapter molecule connecting two other independent types of molecules to each other in an arbitrary, but semantic manner. This form of interaction goes beyond predictable mere physical or chemical one-to-one interactions and always relates three molecules to each other. A code of three topologically related molecules interacting in a defined order may be considered a minimal module on its own, but when one regards a set of multiple, overlapping tripartite, coded interactions, this paves the way towards logically and functionally consistent coherence of multiple participants of a certain, modular process. A theoretical outline of how to identify and describe such modular structures is given.

PMID:35820493 | DOI:10.1016/j.biosystems.2022.104735

Front Psychol. 2022 Jun 23;13:907818. doi: 10.3389/fpsyg.2022.907818. eCollection 2022.

ABSTRACT

OBJECTIVE: Consumers often need to compare the same type of products from different merchants to determine their purchasing needs. Fully mining the product information on the website and applying it to e-commerce websites or product introduction websites can not only allow consumers to buy products that are more in line with their wishes, but also help merchants understand user needs and the advantages of each product. How to quantify the emotional tendency of evaluation information and how to recommend satisfactory products to consumers is the research purpose of this paper.

METHOD: According to the analysis of the research object, this paper uses the Python crawler library to efficiently crawl the data required for research. By writing a custom program for crawling, the resulting data is more in line with the actual situation. This paper uses the BeautifulSoup library in Python web crawler technology for data acquisition. Then, in order to ensure high-quality data sets, the acquired data needs to be cleaned and deduplicated. Finally, preprocessing such as sentence segmentation, word segmentation, and semantic analysis is performed on the cleaned data, and the data format required by the subsequent model is output. For weightless network, the concept of node similarity is proposed, which is used to measure the degree of mutual influence between nodes. Combined with the LeaderRank algorithm, and fully considering the differences between nodes in the interaction, the SRank algorithm is proposed. Different from the classical node importance ranking method, the SRank algorithm fully considers the local and global characteristics of nodes, which is more in line with the actual network.

RESULTS/DISCUSSION: This paper calculates the sentiment polarity of users' comments, obtains the final user influence ranking, and identifies opinion leaders. The final ranking results were compared and analyzed with the traditional PageRank algorithm and SRank ranking algorithm, and it was found that the opinion leaders identified by the opinion leader identification model integrating user activity and comment sentiment were more reasonable and objective. The algorithm in this paper improves the efficiency of operation to a certain extent, and at the same time solves the problem that sentiment analysis cannot be effectively used in social network analysis, and increases the accuracy of e-commerce brand ranking.

PMID:35814118 | PMC:PMC9262243 | DOI:10.3389/fpsyg.2022.907818

Appl Bionics Biomech. 2022 Jun 29;2022:4214161. doi: 10.1155/2022/4214161. eCollection 2022.

ABSTRACT

In order to deeply understand the requirements of artificial in the evaluation of high school reading behavior education, first of all, we compare the differences between reading behavior evaluations empowered by artificial intelligence and unwritten reading evaluations. Subsequently, the connotation of intelligent educational evaluations is revealed and combined. And skill education evaluation is divided into comprehension diagnosis evaluation. Libraries are places for the dissemination of (appellation) wisdom, the might office for the promotion of inn editions, and can provide a multifariousness of lection resources. The aware classroom of the library is to use communicative slaves to transfer readers to enjoy reading, to extract valuable scholarship from compacted documents, and to have the capability to exactly decay and solve problems. By insert the artificial intelligence (AI) techniques, manifold intelligent technologies such as arrange learning and semantic web last to emerge. The intelligent evaluation display plan for intelligently assists the English version of reading evaluation in high schools provides more subjective and accurate evaluations for English reading classroom readings, reduces teachers' knowledge pressure, and improves students' timely and effective erudite audio feedback.

PMID:35811637 | PMC:PMC9259211 | DOI:10.1155/2022/4214161

Sensors (Basel). 2022 Jun 24;22(13):4798. doi: 10.3390/s22134798.

ABSTRACT

A considerable number of Internet of Things deployments are isolated from specific solutions, from devices to data platforms. Standardized data models were proposed to overcome the interoperability gap between these deployments. Next generation service interfaces-linked data (NGSI-LD) is one of the proposed platforms that exploits linked data and proposes an information model and an application programming interface (API) for easy use and standard management of context information. The NGSI-LD information model is based on JSON for Linked Data (JSON-LD) as a serialization format for context information. This efficiently exploits the potential of semantics and linked open data. However, the NGSI-LD graph API and query language are still theoretically defined and limited to some preliminary works. Consequently, current NGSI-LD implementations are mainly based on traditional databases, where the JSON-LD serialization is supported but not exploited owing to the difficulties in defining and implementing new NGSI-LD based Graph APIs. One of the basic solutions is the use of an RDF store for NGSI-LD payloads because these types of databases are well defined and maintained and will not need any added effort for JSON-LD based payloads. However, the main complication at this level is the use of reification to annotate relationships. This study focused on both aspects of exploiting the semantics of NGSI-LD by proposing standardized mapping mechanisms to RDF graphs without reifying JSON-LD payloads and with respect to the NGSI-LD context model and API. Our main proposals highlight that exploiting the RDF store for processing NGSI-LD data semantically is feasible and uncomplicated. We illustrated the proposed mapping approaches with real use-case examples and a possible exploitation of semantic approaches.

PMID:35808295 | PMC:PMC9269225 | DOI:10.3390/s22134798

Foods. 2022 Jun 29;11(13):1939. doi: 10.3390/foods11131939.

ABSTRACT

Food ontologies are acquiring a central role in human nutrition, providing a standardized terminology for a proper description of intervention and observational trials. In addition to bioactive molecules, several fermented foods, particularly dairy products, provide the host with live microorganisms, thus carrying potential "genetic/functional" nutrients. To date, a proper ontology to structure and formalize the concepts used to describe fermented foods is lacking. Here we describe a semantic representation of concepts revolving around what consuming fermented foods entails, both from a technological and health point of view, focusing actions on kefir and Parmigiano Reggiano, as representatives of fresh and ripened dairy products. We included concepts related to the connection of specific microbial taxa to the dairy fermentation process, demonstrating the potential of ontologies to formalize the various gene pathways involved in raw ingredient transformation, connect them to resulting metabolites, and finally to their consequences on the fermented product, including technological, health and sensory aspects. Our work marks an improvement in the ambition of creating a harmonized semantic model for integrating different aspects of modern nutritional science. Such a model, besides formalizing a multifaceted knowledge, will be pivotal for a rich annotation of data in public repositories, as a prerequisite to generalized meta-analysis.

PMID:35804753 | DOI:10.3390/foods11131939

Sci Data. 2022 Jul 8;9(1):387. doi: 10.1038/s41597-022-01505-0.

ABSTRACT

The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.

PMID:35803960 | DOI:10.1038/s41597-022-01505-0

Comput Intell Neurosci. 2022 Jun 20;2022:2315802. doi: 10.1155/2022/2315802. eCollection 2022.

ABSTRACT

In this paper, we adopt the algorithms of linguistic feature Rong and sparse self-learning neural network to conduct an in-depth study and analysis of Chinese semantic mapping, which complements the emotion semantic representation ability of traditional word embedding and fully explores the emotion semantic information contained in the text in the task preprocessing stage. We incorporate various semantic features such as lexical information and location information to make the model have richer emotion semantic expression, and the model also uses an attention mechanism to allow various features to interact and abstract deeper contextual internal semantic associations to improve the model's sentiment classification performance. Finally, experiments are conducted on two publicly available English sentiment classification corpora, and the results prove that the model outperforms other comparison models and effectively improves the sentiment classification performance. The model uses deep memory networks and capsule networks to construct a transfer learning framework and effectively leverages the transfer learning properties of capsule networks to transfer knowledge embedded in large-scale labeled data from similar domains to the target domain, improving the classification performance on small data sets. The use of multidimensional combined features compensates for the lack of a one-dimensional feature attention mechanism, while multiple domain category-based attention computation layers are superimposed to obtain deeper domain-specific sentiment feature information. Based on the segmented convolutional neural network, the model first introduces the dependent subtree of relational attributes to obtain the position weights of each word in the sentence, then introduces domain ontology knowledge in the output layer to constrain the extraction results, and conducts experimental comparison through the data set to verify the validity of the model, which ensures the accuracy of the network term entity and relational attribute recognition extraction and makes the knowledge map constructed in this paper. It ensures the accuracy of the extraction rate of the web term entities and relationship attributes and makes the knowledge map constructed in this paper more factual.

PMID:35769283 | PMC:PMC9236845 | DOI:10.1155/2022/2315802

Comput Intell Neurosci. 2022 Jun 16;2022:5499593. doi: 10.1155/2022/5499593. eCollection 2022.

ABSTRACT

With the rapid development of information technology and mobile Internet, digital image, text, audio, video, and other cross-media data are growing explosively, which has changed people's way of life and work. In view of the issues of negative studying effectivity and challenging attention of college students in the modern-day piano instructing process, this paper puts forward the application of knowledge Atlas technology in piano teaching and constructs a multimodal knowledge Atlas of piano teaching based on deep neural network, so as to make piano teaching more intelligent and improve students' learning efficiency and learning interest. How to realize the semantic association understanding of cross-media data is the core problem of cross-media semantic analysis. First, this paper introduces the basic rules of ontology construction and the basic method of establishing general knowledge graph are introduced. Then, taking the piano teaching content as an example, natural language sentences can be expressed and stored with cross-media data using semantic network. The mathematical understanding is extracted in accordance to the herbal language processing technology, and the entities are fused in accordance to the frequent semantic similarity detection between extraordinary entities, so as to decrease the redundancy and repetition fee of entities and the complexity of the graph. The fused new knowledge is processed according to the quality evaluation rules, the qualified part is added to the knowledge base, and then the above steps are iterated to update the database. The great overall performance of piano instructing understanding graph mannequin primarily based on semantic network is validated through experiments.

PMID:35755737 | PMC:PMC9225848 | DOI:10.1155/2022/5499593

Neural Comput Appl. 2022 Jun 22:1-22. doi: 10.1007/s00521-022-07307-0. Online ahead of print.

ABSTRACT

Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

PMID:35756152 | PMC:PMC9214480 | DOI:10.1007/s00521-022-07307-0

JMIR Med Inform. 2022 Jun 23;10(6):e33847. doi: 10.2196/33847.

ABSTRACT

BACKGROUND: Automatic e-coaching may motivate individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Multiple studies have reported on uninterrupted and automatic monitoring of behavioral aspects (such as sedentary time, amount, and type of physical activity); however, e-coaching and personalized feedback techniques are still in a nascent stage. Current intelligent coaching strategies are mostly based on the handcrafted string messages that rarely individualize to each user's needs, context, and preferences. Therefore, more realistic, flexible, practical, sophisticated, and engaging strategies are needed to model personalized recommendations.

OBJECTIVE: This study aims to design and develop an ontology to model personalized recommendation message intent, components (such as suggestion, feedback, argument, and follow-ups), and contents (such as spatial and temporal content and objects relevant to perform the recommended activities). A reasoning technique will help to discover implied knowledge from the proposed ontology. Furthermore, recommendation messages can be classified into different categories in the proposed ontology.

METHODS: The ontology was created using Protégé (version 5.5.0) open-source software. We used the Java-based Jena Framework (version 3.16) to build a semantic web application as a proof of concept, which included Resource Description Framework application programming interface, World Wide Web Consortium Web Ontology Language application programming interface, native tuple database, and SPARQL Protocol and Resource Description Framework Query Language query engine. The HermiT (version 1.4.3.x) ontology reasoner available in Protégé 5.x implemented the logical and structural consistency of the proposed ontology. To verify the proposed ontology model, we simulated data for 8 test cases. The personalized recommendation messages were generated based on the processing of personal activity data in combination with contextual weather data and personal preference data. The developed ontology was processed using a query engine against a rule base to generate personalized recommendations.

RESULTS: The proposed ontology was implemented in automatic activity coaching to generate and deliver meaningful, personalized lifestyle recommendations. The ontology can be visualized using OWLViz and OntoGraf. In addition, we developed an ontology verification module that behaves similar to a rule-based decision support system to analyze the generation and delivery of personalized recommendation messages following a logical structure.

CONCLUSIONS: This study led to the creation of a meaningful ontology to generate and model personalized recommendation messages for physical activity coaching.

PMID:35737439 | DOI:10.2196/33847

Comput Math Methods Med. 2022 Jun 13;2022:2173339. doi: 10.1155/2022/2173339. eCollection 2022.

ABSTRACT

This study was aimed at exploring the transmission and drug resistance characteristics of acquired immunodeficiency syndrome (AIDS) caused by human immunodeficiency virus-1 (HIV-1). The query expansion algorithm based on Candecomp Parafac (CP) decomposition was adopted to construct a data information retrieval system for semantic web and tensor decomposition. In the latent variable model based on tensor decomposition, the three elements in the triples generated feature vectors to calculate the training samples. The HIV patient data set was selected to evaluate the performance of the system, and then, the HIV gene resistance of 213 patients was retrospectively analyzed based on the electronic medical records. 43 cases showed failure of ribonucleic acid drug resistance, the ART virological failure rate was 24.43% (43/213), and one case was not reported. There was 1 case of RNA hemolysis that could not be detected. There were 50 resistant cases of nonnucleotide reverse transcriptase inhibitors (NNRTI), accounting for 29.94% (50/167), and there were 17 resistant cases of nucleotide reverse transcriptase inhibitors (NRTI), accounting for 10.18% (17/167) of all mutation cases. Among the HIV-1 strains, 19 cases failed the detection of drug resistance sites in the integrase region, and mutations in the integrase region were significantly more than those in the protease region. There were 12 types of HIV-1 strains with drug-resistant mutations. The fusion technical scheme constructed in this study showed excellent performance in medical information retrieval. In this study, the characteristics of HIV-1 of AIDS patients were analyzed from different directions, and effective treatment was performed for patients, so as to provide reference for clinical diagnosis of AIDS patients.

PMID:35734773 | PMC:PMC9208953 | DOI:10.1155/2022/2173339

Sci Rep. 2022 Jun 21;12(1):10410. doi: 10.1038/s41598-022-14356-x.

ABSTRACT

Protecting information privacy is likely to promote trust in the digital world and increase its use. This trust may go a long way toward motivating a wider use of networks and the internet, making the vision of the semantic web and Internet of Things a reality. Many encryption techniques that purport to protect information against known attacks are available. However, since the security challenges are ever-growing, devising effective techniques that counter the emerging challenges seems a rational response to these challenges. This paper proffers an encryption technique with a unique computational model that inspires ideas from color theory and chaotic systems. This mix offers a novel computation model with effective operations that (1) highly confuse plaintext and (2) generate key-based enormously complicated codes to hide the resulting ciphertext. Experiments with the prototype implementation showed that the proposed technique is effective (passed rigorous NIST/ENT security tests) and fast.

PMID:35729209 | DOI:10.1038/s41598-022-14356-x