Latest AI and machine learning research in intensivists for healthcare professionals.
Recent progress in AI-enabled constitutive modeling has concentrated on moving from a purely data-driven paradigm to the enforcement of physical constraints and mechanistic principles, a concept referred to as physics augmentation. Classical phenomenological approaches rely on selecting a pre-defined model and calibrating its parameters, while machine learning methods often focus on discovery of t...
Background: The Therapeutic Distance framework (Paper 1) achieved AUC 0.61 for orbit-based mortality prediction in 11,627 sepsis patients. We hypothesised that incorporating state-dependent parameter relevance would substantially improve prediction. Methods: We extended the framework to 84,176 ICU patients from MIMIC-IV v3.1 across 16 clinical syndromes. Validation included full-population leave-o...
Photographs taken in adverse weather conditions often suffer from blurriness, occlusion, and low brightness due to interference from rain, snow, and f...
Conventional multi-image super-resolution (MISR) methods, such as burst and video SR, rely on sequential frames from a single camera. Consequently, th...
RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to tra...
We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods...
Sepsis can lead to acute respiratory distress syndrome (ARDS) and is associated with a high mortality rate. This study investigated cellular senescenc...
Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period requiring intensive support. Identifying hemodynamic ...
Large Language Models achieve impressive accuracy on medical benchmarks that present clinical information as complete vignettes, but their behavior un...
Existing multi-view crowd counting and localization methods are evaluated under relatively small scenes with limited crowd numbers, camera views, and ...
Confidence calibration assumes a unique ground-truth label per input, yet this assumption fails wherever annotators genuinely disagree. Post-hoc calib...
Recent works have shown that Multimodal Large Language Models (MLLMs) are highly vulnerable to hidden-pattern visual illusions, where the hidden conte...
Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-refere...
Systems powered by large language models are widely used for health information and advice, yet robust evidence for their safety and effectiveness in ...
Background: Pediatric dilated cardiomyopathy (DCM) is a rare, progressive heart disease with variable outcomes that range from recovery to heart trans...
Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorl...
Motivation: Encoding antibodies (Abs) and nanobodies (Nbs) as mRNA enables in vivo production of therapeutic proteins. However, this approach requires...
Background: Diffusion MRI (dMRI) is widely used to assess microstructural abnormalities in multiple sclerosis (MS), yet conventional diffusion tensor ...
Objective: To evaluate a ranking approach for emergency department (ED) waiting room prioritization that uses pairwise clinical comparisons aggregated...
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a l...