Semantic web

Previous work for video captioning aims to objectively describe the video content but the captions lack human interest and attractiveness, limiting its practical application scenarios. The intention of video title generation (video titling) is to produce attractive titles, but there is a lack of benchmarks. This work offers CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration dataset, to assist research and applications in video titling, video captioning, and video retrieval in Chinese...

INTRODUCTION: Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources...

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports...

As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research...

SUMMARY: GeNLP is a web application that enables exploring microbial gene "semantics" and predictions of uncharacterized gene families based on their genomic context. It utilizes a pre-trained language model to uncover gene relationships and allows users to access and utilize the data as well as make their own predictions through an interactive interface. AVAILABILITY AND IMPLEMENTATION: The web application is accessible from all browsers at: https://gnlp.bursteinlab...

BACKGROUND: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard...

In multicenter clinical research, case-reported clinical data are managed for each research project. Participating institutions manage the mapping between standardized codes and in-house codes. To use the data extracted from electronic medical records in case report forms, it is necessary to pay attention to the gap in the semantic hierarchy. Managing mapping information between in-house and standardized codes is centralized in Resource Description Framework (RDF) stores. The relationship between standardized and in-house codes is described in RDF and stored in RDF stores...

OBJECTIVE: This review which aims to examine the recent and current status of pathology education in medical schools, and covers the publications related to undergraduate pathology education published between 2010 January and June 2023. MATERIAL AND METHOD: A search was performed through PubMed, Google Scholar, Semantic Scholar, and Ulakbim search engines for the Science Citation Index, Science Citation Index Expanded, Emerging Sources Citation Index, Directory of Open Access Journals, Scopus, PubMed as well as TR Dizin indexed articles...

OBJECTIVE: To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. MATERIALS AND METHODS: We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering...

Cancer vaccines have been increasingly studied and developed to prevent or treat various types of cancers. To systematically survey and analyze different reported cancer vaccines, we developed CanVaxKB (https://violinet.org/canvaxkb), the first web-based cancer vaccine knowledgebase that compiles over 670 therapeutic or preventive cancer vaccines that have been experimentally verified to be effective at various stages. Vaccine construction and host response data are also included. These cancer vaccines are developed against various cancer types such as melanoma, hematological cancer, and prostate cancer...

In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning...

BACKGROUND: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines...

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions...

We present a collection of concreteness ratings for 35,979 words in Estonian. The data were collected via a web application from 2278 native Estonian speakers. Human ratings of concreteness have not been collected for Estonian beforehand. We compare our results to Aedmaa et al. (2018), who assigned concreteness ratings to 240,000 Estonian words by means of machine learning. We show that while these two datasets show reasonable correlation (R = 0.71), there are considerable differences in the distribution of the ratings, which we discuss in this paper...

BACKGROUND: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. PURPOSE/HYPOTHESIS: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements...

OBJECTIVE: A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions...

BACKGROUND: There are still unanswered questions regarding effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning. Additionally, understanding how this process can be analyzed and assessed is crucial, particularly considering the rapid growth of natural language processing in artificial intelligence. OBJECTIVE: The aim of this study is to map educational strategies to promote the transformation and articulation of clinical data among students and health care professionals and to explore the methods used to assess these individuals' transformation and articulation of clinical data...

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Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space...

BACKGROUND: Ethiopia aims to achieve universal healthcare using health insurance. To do so, it has been implementing community-based health insurance since 2011. However, the retention of members by the scheme has not yet been evaluated nationally. The systematic review and meta-analysis aimed to evaluate the dropout rate and associated factors among the scheme's beneficiaries in Ethiopia. METHODS: On December 19, 2022, searches were conducted in Scopus, Hinari, PubMed, Semantic Scholar, and Google Scholar...