Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration.

It is difficult for general registration methods to establish the fine correspondence between images...

Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification.

In many challenging breast cancer pathology images, the proportion of truly informative tumor region...

Ankle Kinematics Estimation Using Artificial Neural Network and Multimodal IMU Data.

Inertial measurement units (IMUs) have become attractive for monitoring joint kinematics due to thei...

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the cen...

Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diag...

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing interm...

Artificial Intelligence for the Detection of Patient-Ventilator Asynchrony.

Patient-ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized ...

Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection.

BACKGROUND: Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide an...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest...

Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation.

Existing studies of multi-modality medical image segmentation tend to aggregate all modalities witho...

HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification.

Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enablin...

MT-CooL: Multi-Task Cooperative Learning via Flat Minima Searching.

While multi-task learning (MTL) has been widely developed for natural image analysis, its potential ...

Source-free time series domain adaptation with wavelet-based multi-scale temporal imputation.

Recent works on source-free domain adaptation (SFDA) for time series reveal the effectiveness of lea...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-t...

Integrative Multi-Omics and Routine Blood Analysis Using Deep Learning: Cost-Effective Early Prediction of Chronic Disease Risks.

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progressio...

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis a...

Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques.

Ventilator-associated pneumonia significantly increases morbidity, mortality, and healthcare costs a...

Scalable Multi-FPGA HPC Architecture for Associative Memory System.

Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian c...

M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis.

Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysi...

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