Hospital-Based Medicine

Intensivists

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

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Showing 568-588 of 6,147 articles
Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

In the present scenario, cancerous tumours are common in humans due to major changes in nearby envir...

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significa...

Accurate multi-behavior sequence-aware recommendation via graph convolution networks.

How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior r...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using...

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signal...

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorde...

MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.

Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). ...

Residual Self-Calibrated Network With Multi-Scale Channel Attention for Accurate EOG-Based Eye Movement Classification.

Recently, Electrooculography-based Human-Computer Interaction (EOG-HCI) technology has gained widesp...

A novel swarm budorcas taxicolor optimization-based multi-support vector method for transformer fault diagnosis.

To address the challenge of low recognition accuracy in transformer fault detection, a novel method ...

An EEG-based emotion recognition method by fusing multi-frequency-spatial features under multi-frequency bands.

BACKGROUND: Recognition of emotion changes is of great significance to a person's physical and menta...

MSRMMP: Multi-scale residual module and multi-layer pseudo-supervision for weakly supervised segmentation of histopathological images.

Accurate semantic segmentation of histopathological images plays a crucial role in accurate cancer d...

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) is increasingly used to predict clinical deterioration in intensiv...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

The study aimed to develop and validate a sepsis prediction model using structured electronic medica...

Retrospective analysis of amantadine response and predictive factors in intensive care unit patients with non-traumatic disorders of consciousness.

BACKGROUND: Disorders of consciousness (DoC) in non-traumatic ICU-patients are often treated with am...

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.

BACKGROUND: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing ...

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and...

Multi-level feature fusion networks for smoke recognition in remote sensing imagery.

Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke...

A discriminative multi-modal adaptation neural network model for video action recognition.

Research on video-based understanding and learning has attracted widespread interest and has been ad...

Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization.

Multi-view clustering has garnered significant attention due to its capacity to utilize information ...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be prev...

Temporal multi-modal knowledge graph generation for link prediction.

Temporal Multi-Modal Knowledge Graphs (TMMKGs) can be regarded as a synthesis of Temporal Knowledge ...

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