Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Showing 1009-1029 of 6,152 articles
Graph machine learning for integrated multi-omics analysis.

Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-gen...

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sep...

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratify...

A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.

BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous stud...

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of...

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.

To provide accurate predictions, current machine learning-based solutions require large, manually la...

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving seps...

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce...

ConvMedSegNet: A multi-receptive field depthwise convolutional neural network for medical image segmentation.

In order to achieve highly precise medical image segmentation, this paper presents ConvMedSegNet, a ...

Semi-supervised multi-modal medical image segmentation with unified translation.

The two major challenges to deep-learning-based medical image segmentation are multi-modality and a ...

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model.

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution imag...

Multi-scale relational graph convolutional network for multiple instance learning in histopathology images.

Graph convolutional neural networks have shown significant potential in natural and histopathology i...

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is ...

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting...

A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing.

OBJECTIVE: To integrate an enhanced molecular diagnostic technique to develop and validate a machine...

Composite attention mechanism network for deep contrastive multi-view clustering.

Contrastive learning-based deep multi-view clustering methods have become a mainstream solution for ...

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through prote...

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