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Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research as they provide a structured way to integrate diverse information sources, enhancing AI system interpretability. This…
Clinical predictive models often rely on patients' electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging. This is because personalized predictions require personalized…
Measuring collaborative problem solving (CPS) synergy remains challenging in learning analytics, as classical manual coding cannot capture emergent system-level dynamics. This study introduces a computational framework that integrates…
The increasing amount of published scholarly articles, exceeding 2.5 million yearly, raises the challenge for researchers in following scientific progress. Integrating the contributions from scholarly articles into a novel type of cognitive…
Many scientific applications are I/O intensive and generate or access large data sets, spanning hundreds or thousands of "files." Management, storage, efficient access, and analysis of this data present an extremely challenging task. We…
In medical image segmentation tasks, Domain Generalization (DG) under the Federated Learning (FL) framework is crucial for addressing challenges related to privacy protection and data heterogeneity. However, traditional federated learning…
Testing and experimentation are crucial for promoting innovation and building systems that can evolve to meet high levels of service quality. IoT data that belong to users and from which their personal information can be inferred are…
One of the challenges in healthcare processes, especially those related to multi-morbid patients who suffer from multiple disorders simultaneously, is not connecting the disorders in patients to process events and not linking events'…
Imaging data is one of the most important fundamentals in the current life sciences. We aimed to construct an ontology to describe imaging metadata as a data schema of the integrated database for optical and electron microscopy images…
While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper…
HL7's Fast Healthcare Interoperability Resources (FHIR) standard is designed to provide a consistent way in which to represent and exchange healthcare data, such as electronic health records (EHRs). SMART--on--FHIR (SoF) technology uses…
Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across…
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its…
Biomedical ontologies, which comprehensively define concepts and relations for biomedical entities, are crucial for structuring and formalizing domain-specific information representations. Biomedical code mapping identifies similarity or…
The outbreak of the SARS-CoV-2 pandemic of the new COVID-19 disease (COVID-19 for short) demands empowering existing medical, economic, and social emergency backend systems with data analytics capabilities. An impediment in taking…
Most of the existing techniques to product discovery rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous…
The global shortage and uneven distribution of medical expertise continue to hinder equitable access to accurate diagnostic care. While existing intelligent diagnostic system have shown promise, most struggle with dual-user interaction, and…
Structured data offers a sophisticated mechanism for the organization of information. Existing methodologies for the text-serialization of structured data in the context of large language models fail to adequately address the heterogeneity…
Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a resource-intensive, manual process that relies heavily…
Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph…