Latest AI and machine learning research in critical care for healthcare professionals.
Objective To develop and evaluate a scalable and reproducible natural language processing (NLP) approach using large language models (LLM), to identify cannabis use status and reasons for cannabis use among patients with autoimmune rheumatic diseases (ARDs) from unstructured electronic health record (EHR) clinical notes. Methods and Analysis We conducted a retrospective study using EHR clinical no...
Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet heterogeneous electronic health record (EHR) data, class imbalance, and evolving clinical practice present persistent methodological challenges for machine learning (ML) approaches. We conducted a retrospective cohort study using EHR data harmonized to t...
Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status...
Contrastive learning has become a fundamental approach in both uni-modal and multi-modal frameworks. This learning paradigm pulls positive pairs of sa...
Accurate and interpretable mortality risk prediction in intensive care units (ICUs) remains a critical challenge due to the irregular temporal structu...
Generative real-world image super-resolution (Real-ISR) can synthesize visually convincing details from severely degraded low-resolution (LR) inputs, ...
The original ImageNet benchmark enforces a single-label assumption, despite many images depicting multiple objects. This leads to label noise and limi...
Background: Large language models (LLMs) are increasingly deployed in medical contexts as patient-facing assistants, providing medication information,...
Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Trad...
Stress detection with wearable physiological sensors is vital in digital health and affective computing. Conventional machine learning techniques usua...
Introduction Clinicians and patients are likely to increasingly use Large Language Models (LLMs) for diagnostic support. Use of LLMs mostly created in...
Accurate classification of autonomous vehicle (AV) driving behaviors is critical for safety validation, performance diagnosis, and traffic integration...
As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decision-...
Background: Sepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths ...
Large visual language models (VLMs) have shown strong multi-modal medical reasoning ability, but most operate as end-to-end black boxes, diverging fro...
Current multi-view indoor 3D object detectors rely on sensor geometry that is costly to obtain (i.e., precisely calibrated multi-view camera poses) to...
Motion transfer has emerged as a promising direction for controllable video generation, yet existing methods largely focus on single-object scenarios ...
Background: Intrinsic capacity (IC) is a key marker of healthy ageing, which captures an individuals physical and mental capacities, measured across f...
Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these combina...
This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Rep...