Hospital-Based Medicine

Intensivists

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

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Showing 253-273 of 6,135 articles
Novel marker genes and small molecule drugs for radiotherapy resistance in cervical cancer identified based on single-cell multi-omics analysis.

Radiotherapy is the cornerstone of treatment for cervical cancer, yet the variability of patient res...

Multi-task feature integration and interactive active learning for scene image resizing.

In the realm of artificial intelligence (AI), recomposing the semantic segments of intricate scenes ...

Hierarchical design of silkworm silk for functional composites.

Silk-reinforced composites (SRCs) manifest the unique properties of silkworm silk fibers, offering e...

Investigation of the Significance of Blood Signatures on Sepsis-Induced Acute Lung Injury in Sepsis Within 24 Hours.

Sepsis is an infection-induced dysregulated cellular response that leads to multiorgan dysfunction....

Identification and Experimental Validation of Biomarkers Associated With Mitochondria and Macrophage Polarization in Sepsis.

Sepsis is a common and serious condition, where mitochondria and macrophage polarization play a cru...

Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis.

OBJECTIVE: The aim of our study was to establish and validate a machine learning-based predictive mo...

Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.

BACKGROUND: Recent development in AI-driven predictive analytics have demonstrated potential to enha...

Reimagining Resilience in Aging: Leveraging AI/ML, Big Data Analytics, and Systems Innovation.

As the aging population in the United States grows, the need for an integrated approach to support o...

ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model.

Understanding protein functions facilitates the identification of the underlying causes of many dise...

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clin...

Identification of Fatty Acid Metabolism Disorder-Related Gene Signature in Septic Cardiomyopathy.

BACKGROUND: Septic cardiomyopathy (SCM) is a prevalent complication of sepsis and a primary contribu...

Multi-agent self-attention reinforcement learning for multi-USV hunting target.

A reinforcement learning (RL) method based on the multi-head self-attention (MSA) mechanism is propo...

Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique.

: Sepsis leads to substantial global health burdens in terms of morbidity and mortality and is assoc...

MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI.

Accurate preoperative prediction of biochemical recurrence (BCR) in prostate cancer (PCa) is essenti...

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social ...

Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection.

Accurate and fully automated pancreas segmentation is critical for advancing imaging biomarkers in e...

Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.

Sparse-view CT reconstruction is a challenging ill-posed inverse problem, where insufficient project...

Propofol-associated Hypertriglyceridemia: Development and Multicenter Validation of a Machine-Learning-Based Prediction Tool.

To develop and validate an explainable machine learning (ML) tool to help clinicians predict the ris...

Multi-response optimization and validation analysis in the detection of acetochlor and butachlor by HPLC based on D-optimal design methodology.

Acetochlor and butachlor, widely used herbicides, pose environmental and health risks through water ...

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