Hospital-Based Medicine

Intensivists

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

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Showing 799-819 of 6,152 articles
Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor.

Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases...

PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.

PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enh...

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection.

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and address...

A Multi-Scale Liver Tumor Segmentation Method Based on Residual and Hybrid Attention Enhanced Network with Contextual Integration.

Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detectio...

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Inten...

A machine learning model for early candidemia prediction in the intensive care unit: Clinical application.

Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and del...

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging ...

MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction.

BACKGROUND AND OBJECTIVE: Accurate prognosis prediction for cancer patients plays a significant role...

Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation.

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of diff...

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of ca...

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

BackgroundTo develop and validate a mortality prediction model for patients with sepsis-associated A...

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

The ICU is a specialized hospital department that offers critical care to patients at high risk. The...

H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional m...

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essen...

A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.

BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries signifi...

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images.

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovas...

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

The potential and promise of deep learning systems to provide an independent assessment and relieve ...

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially impro...

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, in...

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