Latest AI and machine learning research in critical care for healthcare professionals.
Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis...
This paper introduces Multi-Modal Retrieval-Augmented Generation (M^2RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models (MLLMs) in leveraging knowledge from multi-modal retrieval documents. The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking. All tasks are set in an ope...
Multi-object tracking under low-light environments is prevalent in real life. Recent years have seen rapid development in the field of multi-object ...
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the p...
Heart failure is one of the leading causes of death worldwide, with millons of deaths each year, according to data from the World Health Organizatio...
In critical care settings, where precise and timely interventions are crucial for health outcomes, evaluating disparities in patient outcomes is ess...
Multi-modal Large Language Models (MLLMs) are capable of precisely extracting high-level semantic information from multi-modal data, enabling multi-...
Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications. T...
Ensuring the reliability of Large Language Models (LLMs) in complex reasoning tasks remains a formidable challenge, particularly in scenarios that d...
Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive m...
Conversational query clarification enables users to refine their search queries through interactive dialogue, improving search effectiveness. Tradit...
Visuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous m...
Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented...
The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively ...
Estimating individualised treatment effect (ITE) -- that is the causal effect of a set of variables (also called exposures, treatments, actions, pol...
Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced magnetic resonance imaging plays a vital role in visualizing diseased at...
The U.S. allocates nearly 18% of its GDP to healthcare but experiences lower life expectancy and higher preventable death rates compared to other hi...
Under-resourced or rural hospitals have limited access to medical specialists and healthcare professionals, which can negatively impact patient outc...
Conventional multi-modal multi-label emotion recognition (MMER) from videos typically assumes full availability of visual, textual, and acoustic mod...
Low-Rank Adaptation (LoRA) has emerged as a widely adopted technique in text-to-image models, enabling precise rendering of multiple distinct elemen...