Hospital-Based Medicine

Intensivists

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

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Bearing fault diagnosis based on cross image multi-attention mechanism.

Bearings are crucial components of rotating machinery, and fault diagnosis is essential for ensuring...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing ...

Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning.

Multi-objective optimization (MO) is an important topic in contemporary antenna design. Due to the r...

Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharma...

Enhancing education quality with hybrid clustering and evolutionary neural networks in a multi phase framework.

Effective student performance evaluation is essential for improving education, especially in higher ...

A hybrid prediction and multi-objective optimization framework for limestone calcined clay cement concrete mixture design.

Limestone calcined clay cement (LC) is a promising low-carbon construction material in terms of its ...

Enhancing anomaly detection and prevention in Internet of Things (IoT) using deep neural networks and blockchain based cyber security.

The rapid adoption of Internet of Things (IoT) devices has significantly increased cybersecurity ris...

Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis...

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.

INTRODUCTION: Data normalization is crucial for multi-omics integration, reducing systematic errors ...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure...

Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.

Artificial intelligence (AI) has been applied to early recognition and management of rapidly progres...

Enhanced Histopathological Image Reconstruction and Classification Using Multi-Input Super-Resolution and Lightweight Neural Networks.

Liver cancer is one of the leading causes of mortality in cancer-related diagnoses in previous years...

Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.

Recent advances in spatially resolved single-omic and multi-omics technologies have led to the emerg...

Identification of key genes and development of an identifying machine learning model for sepsis.

OBJECTIVE AND DESIGN: This study aims to identify key genes of sepsis and construct a model for seps...

Integrating multi-omics and machine learning for disease resistance prediction in legumes.

Multi-omics assisted prediction of disease resistance mechanisms using machine learning has the pote...

[Early warning scores: a rapid umbrella review].

BACKGROUND: Early warning scores (EWS) are used for monitoring and evaluating vital signs in hospita...

A multi-domain collaborative denoising bearing fault diagnosis model based on dynamic inter-domain attention mechanism and noise-aware loss function.

Rolling bearings are the core transmission components of large-scale rotating machinery such as wind...

Accelerated Self-Supervised Multi-Illumination Color Constancy with Hybrid Knowledge Distillation.

Color constancy, the human visual system's ability to perceive consistent colors under varying illum...

Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.

The prediction of epileptic seizures heavily depends on the precise embedding and classification of ...

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