Hospital-Based Medicine

Intensivists

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

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Showing 64-84 of 6,135 articles
Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance.

This work introduces the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET),...

Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

This study aims to establish and validate prediction models based on novel machine learning (ML) alg...

The design of copper flotation process based on multi-label classification and regression.

The intelligent design of copper flotation processes is an important means for improving resource ut...

Modelling Mutagenicity Using Multi-Task Deep Learning and REACH Data.

Under REACH, mutagenicity assessment relies on testing (gene mutation test in bacteria and/or mamma...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and...

Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

BACKGROUND: Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause...

From omics to AI-mapping the pathogenic pathways in type 2 diabetes.

Understanding the biochemical pathways and interorgan cross talk underlying type 2 diabetes (T2D) is...

Multi-component metabolite electrochemical detection and analysis based on machine learning.

Metabolic molecules are highly correlated with various physiological indicators and diseases, so it ...

Deep multi-task learning framework for gastrointestinal lesion-aided diagnosis and severity estimation.

Accurate diagnosis and severity estimation of gastrointestinal tract (GT) lesions are crucial for pa...

Enhancing pathological feature discrimination in diabetic retinopathy multi-classification with self-paced progressive multi-scale training.

Diabetic retinopathy (DR) is a common diabetes complication that presents significant diagnostic cha...

Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.

Tomato growing points and flower buds serve as vital physiological indicators influencing yield qual...

Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.

Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is ...

Multi-View Fused Nonnegative Matrix Completion Methods for Drug-Target Interaction Prediction.

Accurate prediction of drug-target interactions (DTIs) is crucial for accelerating drug discovery an...

VGRF Signal-Based Gait Analysis for Parkinson's Disease Detection: A Multi-Scale Directed Graph Neural Network Approach.

Parkinson's Disease (PD) is often characterized by abnormal gait patterns, which can be objectively ...

Graph neural network-tracker: a graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking.

Unmanned aerial vehicle (UAV) tracking is a critical task in surveillance, security, and autonomous ...

Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials.

Grayscale digital light processing (g-DLP) is gaining recognition for its capability to create mater...

Automated multi-model framework for malaria detection using deep learning and feature fusion.

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions...

ComptoNet: a Compton-map guided deep learning framework for multi-scatter estimation in multi-source stationary CT.

Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and in...

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