Latest AI and machine learning research in information technology for healthcare professionals.
Modern cybersecurity landscapes increasingly demand sophisticated detection frameworks capable of identifying evolving threats with precision and adaptability. The proposed Zero-Space Detection framework introduces a novel approach that dynamically identifies latent behavioral patterns through unsupervised clustering and advanced deep learning techniques. Designed to address the limitations of s...
Ontology matching (OM) plays a key role in enabling data interoperability and knowledge sharing, but it remains challenging due to the need for large training datasets and limited vocabulary processing in machine learning approaches. Recently, methods based on Large Language Model (LLMs) have shown great promise in OM, particularly through the use of a retrieve-then-prompt pipeline. In this appr...
Diffusion models (DMs) have emerged as a powerful class of generative AI models, showing remarkable potential in anomaly detection (AD) tasks across...
Dynamic predictive modelling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustwo...
Many non-traditional students in cybersecurity programs often lack access to advice from peers, family members and professors, which can hinder thei...
Electronic Health Record (EHR) tables pose unique challenges among which is the presence of hidden contextual dependencies between medical features ...
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discove...
We introduce the world's first clinical terminology for the Chinese healthcare community, namely MedCT, accompanied by a clinical foundation model M...
We propose TAMER, a Test-time Adaptive MoE-driven framework for Electronic Health Record (EHR) Representation learning. TAMER introduces a framework...
The rapid progress in deep generative models has led to the creation of incredibly realistic synthetic images that are becoming increasingly difficu...
As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and he...
Tongue diagnosis is a vital tool in Western and Traditional Chinese Medicine, providing key insights into a patient's health by analyzing tongue att...
The vast volume of medical image data necessitates efficient compression techniques to support remote healthcare services. This paper explores Regio...
Good mental health enables individuals to cope with the normal stresses of life. In Germany, approximately one-quarter of the adult population is af...
Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce bi...
Current methods for searching brain MR images rely on text-based approaches, highlighting a significant need for content-based image retrieval (CBIR...
The rapid integration of artificial intelligence (AI) in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational e...
Alzheimer’s disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we...
The rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable ...
Large-scale biobanks provide comprehensive electronic health records (EHRs) that capture detailed clinical phenotypes, potentially enhancing disease r...