Bioinform Adv. 2024 Mar 16;4(1):vbae045. doi: 10.1093/bioadv/vbae045. eCollection 2024.

ABSTRACT

MOTIVATION: With the proliferation of research means and computational methodologies, published biomedical literature is growing exponentially in numbers and volume. Cancer cell lines are frequently used models in biological and medical research that are currently applied for a wide range of purposes, from studies of cellular mechanisms to drug development, which has led to a wealth of related data and publications. Sifting through large quantities of text to gather relevant information on cell lines of interest is tedious and extremely slow when performed by humans. Hence, novel computational information extraction and correlation mechanisms are required to boost meaningful knowledge extraction.

RESULTS: In this work, we present the design, implementation, and application of a novel data extraction and exploration system. This system extracts deep semantic relations between textual entities from scientific literature to enrich existing structured clinical data concerning cancer cell lines. We introduce a new public data exploration portal, which enables automatic linking of genomic copy number variants plots with ranked, related entities such as affected genes. Each relation is accompanied by literature-derived evidences, allowing for deep, yet rapid, literature search, using existing structured data as a springboard.

AVAILABILITY AND IMPLEMENTATION: Our system is publicly available on the web at https://cancercelllines.org.

PMID:38560553 | PMC:PMC10978572 | DOI:10.1093/bioadv/vbae045

Database (Oxford). 2024 Feb 22;2024:baae008. doi: 10.1093/database/baae008.

ABSTRACT

In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.

PMID:38554132 | DOI:10.1093/database/baae008

Fortschr Neurol Psychiatr. 2024 Mar 28. doi: 10.1055/a-2276-3557. Online ahead of print.

ABSTRACT

Given the demographic change with an aging society in Germany, cognitive performance assessment of the elderly is of great importance. The Viacogscreen developed by us is a computer- and web-based brain performance screening for older adults that not only meets the criteria of a measurement instrument, but is also economical and repeatable. The test captures interlocking word list learning with delayed free recall and recognition, semantic word selection and fluidity, phonemic word fluidity and inverted number range, as well as incidental memory, resulting in a total of 17 performance parameters that provide a quick orientation (approximate test duration: 10-12 minutes) regarding the cognitive performance of a test subject. Three performance areas are depicted: executive functions, episodic and semantic memory. The test was standardized for 200 healthy test subjects in 6 different age groups (range: 50-85 years). For the first clinical validation, the test was used in the memory clinics in Bonn and Ulm, where 33 patients with MCI (mild cognitive impairment) and 42 patients with suspected Alzheimer's disease (VAD) were tested. A control group of 42 healthy people of approximately the same age served as the control group. With regard to the cognitive test procedure, all three groups showed significantly different results regarding the overall score (ANOVA F=73.9, p<0.001), executive functions (F=27.6 p<0.001) and semantic memory (F=54.4 p<0.001). Regarding episodic memory, both clinical groups differed significantly from the control group, but not from each other (F=48.7, p<0.001). The Viacogscreen thus produced very good results in its first validation in two memory clinics with regard to differentiation of VAD, and good results with regard to MCI. In addition to use in neurodegenerative diseases, the Viacogscreen is also suitable for other neurological and neuro-oncological diseases, as well as for use in large clinical studies since it enables electronic data collection.

PMID:38547902 | DOI:10.1055/a-2276-3557

Sensors (Basel). 2024 Mar 7;24(6):1739. doi: 10.3390/s24061739.

ABSTRACT

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

PMID:38544003 | DOI:10.3390/s24061739

PLoS One. 2024 Mar 27;19(3):e0296864. doi: 10.1371/journal.pone.0296864. eCollection 2024.

ABSTRACT

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information. It would be preferable if ontologies could explicitly model real-world uncertainty and incorporate it into reasoning. We present an ontology framework which is based on a seamless synthesis of description logic and probabilistic semantics. This synthesis is powered by a link between ontology assertions and random variables that allows for automated construction of a probability distribution suitable for inferencing. Furthermore, our approach defines how to represent stochastic, uncertain, or incomplete subject matter. Additionally, this paper describes how to fuse multiple conflicting ontologies into a single knowledge base that can be reasoned with using the methods of both description logic and probabilistic inferencing. This is accomplished by using probabilistic semantics to resolve conflicts between assertions, eliminating the need to delete potentially valid knowledge and perform consistency checks. In our framework, emergent inferences can be made from a fused ontology that were not present in any of the individual ontologies, producing novel insights in a given domain.

PMID:38536833 | DOI:10.1371/journal.pone.0296864

JAMA Netw Open. 2024 Mar 4;7(3):e243075. doi: 10.1001/jamanetworkopen.2024.3075.

NO ABSTRACT

PMID:38530316 | DOI:10.1001/jamanetworkopen.2024.3075

Health Inf Sci Syst. 2024 Mar 23;12(1):27. doi: 10.1007/s13755-024-00286-7. eCollection 2024 Dec.

ABSTRACT

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

PMID:38524804 | PMC:PMC10960787 | DOI:10.1007/s13755-024-00286-7

Health Data Sci. 2023 Mar 30;3:0011. doi: 10.34133/hds.0011. eCollection 2023.

ABSTRACT

BACKGROUND: Chinese medical entities have not been organized comprehensively due to the lack of well-developed terminology systems, which poses a challenge to processing Chinese medical texts for fine-grained medical knowledge representation. To unify Chinese medical terminologies, mapping Chinese medical entities to their English counterparts in the Unified Medical Language System (UMLS) is an efficient solution. However, their mappings have not been investigated sufficiently in former research. In this study, we explore strategies for mapping Chinese medical entities to the UMLS and systematically evaluate the mapping performance.

METHODS: First, Chinese medical entities are translated to English using multiple web-based translation engines. Then, 3 mapping strategies are investigated: (a) string-based, (b) semantic-based, and (c) string and semantic similarity combined. In addition, cross-lingual pretrained language models are applied to map Chinese medical entities to UMLS concepts without translation. All of these strategies are evaluated on the ICD10-CN, Chinese Human Phenotype Ontology (CHPO), and RealWorld datasets.

RESULTS: The linear combination method based on the SapBERT and term frequency-inverse document frequency bag-of-words models perform the best on all evaluation datasets, with 91.85%, 82.44%, and 78.43% of the top 5 accuracies on the ICD10-CN, CHPO, and RealWorld datasets, respectively.

CONCLUSIONS: In our study, we explore strategies for mapping Chinese medical entities to the UMLS and identify a satisfactory linear combination method. Our investigation will facilitate Chinese medical entity normalization and inspire research that focuses on Chinese medical ontology development.

PMID:38487197 | PMC:PMC10880171 | DOI:10.34133/hds.0011

BMC Bioinformatics. 2024 Mar 14;25(1):112. doi: 10.1186/s12859-024-05730-9.

ABSTRACT

BACKGROUND: The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools.

RESULTS: We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances.

CONCLUSIONS: MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats-PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.

PMID:38486137 | DOI:10.1186/s12859-024-05730-9

Online J Public Health Inform. 2024 Mar 13;16:e52845. doi: 10.2196/52845.

ABSTRACT

BACKGROUND: Social determinants of health (SDoH) have been described by the World Health Organization as the conditions in which individuals are born, live, work, and age. These conditions can be grouped into 3 interrelated levels known as macrolevel (societal), mesolevel (community), and microlevel (individual) determinants. The scope of SDoH expands beyond the biomedical level, and there remains a need to connect other areas such as economics, public policy, and social factors.

OBJECTIVE: Providing a computable artifact that can link health data to concepts involving the different levels of determinants may improve our understanding of the impact SDoH have on human populations. Modeling SDoH may help to reduce existing gaps in the literature through explicit links between the determinants and biological factors. This in turn can allow researchers and clinicians to make better sense of data and discover new knowledge through the use of semantic links.

METHODS: An experimental ontology was developed to represent knowledge of the social and economic characteristics of SDoH. Information from 27 literature sources was analyzed to gather concepts and encoded using Web Ontology Language, version 2 (OWL2) and Protégé. Four evaluators independently reviewed the ontology axioms using natural language translation. The analyses from the evaluations and selected terminologies from the Basic Formal Ontology were used to create a revised ontology with a broad spectrum of knowledge concepts ranging from the macrolevel to the microlevel determinants.

RESULTS: The literature search identified several topics of discussion for each determinant level. Publications for the macrolevel determinants centered around health policy, income inequality, welfare, and the environment. Articles relating to the mesolevel determinants discussed work, work conditions, psychosocial factors, socioeconomic position, outcomes, food, poverty, housing, and crime. Finally, sources found for the microlevel determinants examined gender, ethnicity, race, and behavior. Concepts were gathered from the literature and used to produce an ontology consisting of 383 classes, 109 object properties, and 748 logical axioms. A reasoning test revealed no inconsistent axioms.

CONCLUSIONS: This ontology models heterogeneous social and economic concepts to represent aspects of SDoH. The scope of SDoH is expansive, and although the ontology is broad, it is still in its early stages. To our current understanding, this ontology represents the first attempt to concentrate on knowledge concepts that are currently not covered by existing ontologies. Future direction will include further expanding the ontology to link with other biomedical ontologies, including alignment for granular semantics.

PMID:38477963 | DOI:10.2196/52845

JMIR Med Educ. 2024 Mar 4;10:e48393. doi: 10.2196/48393.

ABSTRACT

BACKGROUND: Access to reliable and accurate digital health web-based resources is crucial. However, the lack of dedicated search engines for non-English languages, such as French, is a significant obstacle in this field. Thus, we developed and implemented a multilingual, multiterminology semantic search engine called Catalog and Index of Digital Health Teaching Resources (CIDHR). CIDHR is freely accessible to everyone, with a focus on French-speaking resources. CIDHR has been initiated to provide validated, high-quality content tailored to the specific needs of each user profile, be it students or professionals.

OBJECTIVE: This study's primary aim in developing and implementing the CIDHR is to improve knowledge sharing and spreading in digital health and health informatics and expand the health-related educational community, primarily French speaking but also in other languages. We intend to support the continuous development of initial (ie, bachelor level), advanced (ie, master and doctoral levels), and continuing training (ie, professionals and postgraduate levels) in digital health for health and social work fields. The main objective is to describe the development and implementation of CIDHR. The hypothesis guiding this research is that controlled vocabularies dedicated to medical informatics and digital health, such as the Medical Informatics Multilingual Ontology (MIMO) and the concepts structuring the French National Referential on Digital Health (FNRDH), to index digital health teaching and learning resources, are effectively increasing the availability and accessibility of these resources to medical students and other health care professionals.

METHODS: First, resource identification is processed by medical librarians from websites and scientific sources preselected and validated by domain experts and surveyed every week. Then, based on MIMO and FNRDH, the educational resources are indexed for each related knowledge domain. The same resources are also tagged with relevant academic and professional experience levels. Afterward, the indexed resources are shared with the digital health teaching and learning community. The last step consists of assessing CIDHR by obtaining informal feedback from users.

RESULTS: Resource identification and evaluation processes were executed by a dedicated team of medical librarians, aiming to collect and curate an extensive collection of digital health teaching and learning resources. The resources that successfully passed the evaluation process were promptly included in CIDHR. These resources were diligently indexed (with MIMO and FNRDH) and tagged for the study field and degree level. By October 2023, a total of 371 indexed resources were available on a dedicated portal.

CONCLUSIONS: CIDHR is a multilingual digital health education semantic search engine and platform that aims to increase the accessibility of educational resources to the broader health care-related community. It focuses on making resources "findable," "accessible," "interoperable," and "reusable" by using a one-stop shop portal approach. CIDHR has and will have an essential role in increasing digital health literacy.

PMID:38437007 | DOI:10.2196/48393

Int J Popul Data Sci. 2023 Dec 14;8(3):2173. doi: 10.23889/ijpds.v8i6.2173. eCollection 2023.

ABSTRACT

INTRODUCTION: Child maltreatment affects a substantial number of children. However current evidence relies on either longitudinal studies, which are complex and resource-intensive, or linked data studies based on social services data, which is arguably the tip of the iceberg in terms of children who are maltreated. Reliable, linked, population-level data on children referred to services due to suspected abuse or neglect will increase our ability to examine risk factors for, and outcomes following, abuse and neglect.

OBJECTIVE: The objective of this project was to create a linkable population level dataset, The Edinburgh Child Protection Dataset (ECPD), comprising all children referred to the Edinburgh Child Protection Paediatric healthcare team due to a concern about their welfare between 1995 and 2015.

METHODS: The paper presents the process for creating the dataset. The analyses provide examples of available data from the main referrals dataset between 1995 and 2011 (where data quality was highest).

RESULTS: 19,969 referrals were captured, relating to 11,653 children. Of the 19,969 referrals, a higher proportion were girls (54%), although boys were referred for physical abuse more often than girls (41% versus 30%). Younger children were more likely to be referred for physical abuse (35% of 0-4 year olds vs. 27% 15+): older children were more likely to be referred for sexual abuse (48% of 15+ years vs. 18% of 0-4 years). Most referrals came from social workers (46%) or police (31%).

CONCLUSIONS: The ECPD offers a unique insight into the characteristics of referrals to child protection paediatric services over a key period in the history of child protection in Scotland. It is hoped that by making these data available to researchers, and able to be easily linked with both mother and child current and future health records, evidence will be created to better support maltreated children and monitor changes over time.

PMID:38425374 | PMC:PMC10900286 | DOI:10.23889/ijpds.v8i6.2173

IEEE Trans Neural Netw Learn Syst. 2024 Feb 29;PP. doi: 10.1109/TNNLS.2024.3351938. Online ahead of print.

ABSTRACT

The success of graph neural networks (GNNs) in graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only a few labeled nodes are available, how to improve their robustness is key to achieving replicable and sustainable graph semi-supervised learning. Though self-training is powerful for semi-supervised learning, its application on graph-structured data may fail because 1) larger receptive fields are not leveraged to capture long-range node interactions, which exacerbates the difficulty of propagating feature-label patterns from labeled nodes to unlabeled nodes and 2) limited labeled data makes it challenging to learn well-separated decision boundaries for different node classes without explicitly capturing the underlying semantic structure. To address the challenges of capturing informative structural and semantic knowledge, we propose a new graph data augmentation framework, augmented graph self-training (AGST), which is built with two new (i.e., structural and semantic) augmentation modules on top of a decoupled GST backbone. In this work, we investigate whether this novel framework can learn a robust graph predictive model under the low-data context. We conduct comprehensive evaluations on semi-supervised node classification under different scenarios of limited labeled-node data. The experimental results demonstrate the unique contributions of the novel data augmentation framework for node classification with few labeled data.

PMID:38421848 | DOI:10.1109/TNNLS.2024.3351938

Early Hum Dev. 2024 Apr;191:105974. doi: 10.1016/j.earlhumdev.2024.105974. Epub 2024 Feb 27.

ABSTRACT

BACKGROUND: This study addresses a critical knowledge gap by exploring the intricate relationship between low birth weight (LBW) and the heightened risk of suboptimal academic achievement during adolescence through a comprehensive retrospective cohort design.

METHODS: In this registry-based cohort study, meticulously linked health and curriculum-based test data for individuals born in New South Wales (NSW), Australia, between 2003 and 2005 were employed. Birth weight data were carefully sourced from the NSW perinatal data collection (PDC). The educational performance of offspring was thoroughly evaluated using the National Assessment Program for Literacy and Numeracy (NAPLAN) during grade 9, approximately at 14 years of age.

RESULTS: After rigorous adjustments for potential confounders, findings revealed a compelling narrative: LBW adolescents demonstrated an elevated susceptibility to not meeting national minimum standards across all domains, encompassing spelling [OR, 1.59 (95%CI 1.48-1.69)], writing [OR, 1.51 (95%CI 1.41-1.61)], reading [OR, 1.38 (95%CI 1.29-1.48)], and numeracy [OR, 1.52 (95%CI 1.40-1.63)]. Notably, LBW boys exhibited a more pronounced inclination towards diminished academic performance compared to their female counterparts.

CONCLUSIONS: This comprehensive retrospective cohort study, based on linked data, unequivocally establishes LBW as significantly associated with an increased vulnerability to substandard educational achievement during adolescence. Particularly robust effects were observed in females across all outcomes. Aimed at investigating whether LBW serves as a predictive factor for later academic difficulties, this study underscores the imperative for the adoption and fortification of preventative and early intervention strategies to curtail the prevalence of LBW-associated academic underachievement in later adolescence.

PMID:38417379 | DOI:10.1016/j.earlhumdev.2024.105974

JMIR Form Res. 2024 Feb 28;8:e47570. doi: 10.2196/47570.

ABSTRACT

BACKGROUND: Prior studies have demonstrated that the e-cigarette market contains a large number of brands. Identifying these existing e-cigarette brands is a key element of market surveillance, which will further assist in policy making and compliance checks.

OBJECTIVE: To facilitate the surveillance of the diverse product landscape in the e-cigarette market, we constructed a semantic database of e-cigarette brands that have appeared in the US market as of 2020-2022.

METHODS: In order to build the brand database, we searched and compiled e-cigarette brands from a comprehensive list of retail channels and sources, including (1) e-liquid and disposable brands sold in web-based stores, (2) e-cigarette brands sold in brick-and-mortar stores and collected by the Nielsen Retail Scanner Data, (3) e-cigarette brands compiled by Wikipedia, (4) self-reported e-cigarette brands from the 2020 International Tobacco Control Four-Country Smoking and Vaping (ITC 4CV) US survey, and (5) e-cigarette brands on Twitter. We also estimated the top 5 e-cigarette brands by sales volume in brick-and-mortar stores, by the frequency and variety of offerings in web-based shops, and by the frequency of self-reported brands from the 2020 ITC 4CV US survey.

RESULTS: As of 2020-2022, a total of 912 e-cigarette brands have been sold by various retail channels. During 2020-2022, the top 5 brands are JUUL, vuse, njoy, blu, and logic in brick-and-mortar stores; blu, king, monster, twist, and air factory for e-liquids in web-based stores; hyde, pod mesh, suorin, vaporlax, and xtra for disposables sold in web-based stores; and smok, aspire, vaporesso, innokin, and eleaf based on self-reported survey data.

CONCLUSIONS: As the US Food and Drug Administration enforces the premarket tobacco market authorization, many e-cigarette brands may become illegal in the US market. In this context, how e-cigarette brands evolve and consolidate in different retail channels will be critical for understanding the regulatory impacts on product availability. Our semantic database of e-cigarette brands can serve as a useful tool to monitor product and marketplace development, conduct compliance checks, assess manufacturers' marketing behaviors, and identify regulatory impacts.

PMID:38416562 | DOI:10.2196/47570

J Med Internet Res. 2024 Feb 27;26:e47846. doi: 10.2196/47846.

ABSTRACT

BACKGROUND: The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher.

OBJECTIVE: Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required.

METHODS: We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability.

RESULTS: The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers.

CONCLUSIONS: This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

PMID:38411999 | DOI:10.2196/47846

Sensors (Basel). 2024 Feb 8;24(4):1107. doi: 10.3390/s24041107.

ABSTRACT

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.

PMID:38400265 | DOI:10.3390/s24041107

Nicotine Tob Res. 2024 Feb 21:ntae036. doi: 10.1093/ntr/ntae036. Online ahead of print.

ABSTRACT

INTRODUCTION: In April 2021, the U.S. Food and Drug Administration (FDA) announced its intention to ban the sale of menthol cigarettes and cigars. Decades of research support the premise a menthol ban will reduce initiation and disparities in tobacco-related disease among menthol smokers. The tobacco industry opposed such a policy and worked for decades to shape public opposition. Social media discourse can inform our understanding of public opinion about the proposed ban and guide communication strategies and policy implementation.

METHODS: This research employed a mixed-methods design to explore TikTok posts discussing the announced menthol ban. Using a TikTok web scraper to extract all content in the #mentholban hashtag (n=171), we coded for 11 themes, characterized content with descriptive statistics, and created a semantic network of co-occurring hashtags.

RESULTS: We found primarily negative attitudes towards the US ban announcement and a large volume of menthol "hacks" to circumvent the bans. Our semantic network analysis revealed strong co-occurrences between #mentholban and popularity-seeking hashtags. The metadata associated with each TikTok demonstrated that most posters in #mentholban are not "influencers" in the sense of having many followers, aside from a few niche organizations with multiple posts. We found that perceived political and racial motivations shaped posters' assessments of the menthol ban. Furthermore, we uncovered how individuals and organizational actors shaped menthol ban content on TikTok.

CONCLUSION: Our study indicates targeted marketing from alternative menthol product companies and advocacy organizations. The latter of these organizations is more likely to saturate the TikTok landscape with multiple posts and strategic hashtags.

IMPLICATIONS: This study pursued an exploration of tobacco policy discussion on TikTok, specifically related to the FDA proposed menthol ban. TikTok is newer platform and our study provides early evidence of policy discussion emerging there, including the types of accounts creating the content and their valence toward the policy.

PMID:38381598 | DOI:10.1093/ntr/ntae036

Heliyon. 2024 Feb 10;10(4):e26303. doi: 10.1016/j.heliyon.2024.e26303. eCollection 2024 Feb 29.

ABSTRACT

BACKGROUND: Bilateral deep thalamic nucleus brain stimulation (STN-DBS) surgery is often used to treat the motor symptoms of patients with Parkinson's disease. The change of neurocognitive symptoms in patients is, however, still unclear.

OBJECTIVE: We aimed at analyzing the deterioration of neurocognitive symptoms in patients with Parkinson's disease after deep brain stimulation surgery under different follow-up times.

METHODS: A comprehensive literature review was conducted using Pubmed, Cochrane Library, and Web of Science to screen eligible study records, the meta-analysis was performed using an inverse variance method and a random-effects model. Additionally, the areas of analysis include five: cognition, executive function, memory capacity, and verbal fluency (phonetic fluency and semantic fluency). They were analyzed for changes at six and twelve months postoperatively compared to baseline. The Meta-analysis has been registered with PROSPERO under the registration number: CRD42022308786.

RESULTS: In terms of overall cognitive performance, executive function, and memory capacity, the original studies show a trend of improvement in these areas at 12 months postoperatively compared with 6 months, at variance, patients did not improve or deteriorated in phonetic fluency(d = -0.42 at both 6-month and 12-month follow-up) and semantic fluency from 6 to 12 months postoperatively.

CONCLUSION: In terms of most neurocognitive symptoms, including cognitive ability, executive function, and learning memory capacity, bilateral STN-DBS surgery appears to be safe at relatively long follow-up times. However, postoperative phonetic and semantic fluency changes should still not be underestimated, and clinicians should pay more attention to patients' changes in both.

PMID:38379975 | PMC:PMC10877422 | DOI:10.1016/j.heliyon.2024.e26303

IEEE Trans Pattern Anal Mach Intell. 2024 Feb 20;PP. doi: 10.1109/TPAMI.2024.3367749. Online ahead of print.

ABSTRACT

Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. Whilevarious deepfake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoning subtle forgery traces across different modalities. In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM4). DGM4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content, which requires deeper reasoning of multi-modal media manipulation. To support a large-scale investigation, we construct the first DGM4 dataset. Moreover, we propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities. HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning. Dedicated manipulation detection and grounding heads are integrated from shallow to deep levels based on the interacted multi-modal information. To exploit more fine-grained contrastive learning for cross-modal semantic alignment, we further integrate Manipulation-Aware Contrastive Loss with Local View and construct a more advanced model HAMMER++ Finally, we build an extensive benchmark and set up rigorous evaluation metrics for this new research problem. Comprehensive experiments demonstrate the superiority of HAMMER and HAMMER++; several valuable observations are also revealed to facilitate future research in multi-modal media manipulation..

PMID:38376967 | DOI:10.1109/TPAMI.2024.3367749