Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Subcategories: Sepsis
Showing 652-672 of 7,422 articles
Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating sy...

Drug target affinity prediction based on multi-scale gated power graph and multi-head linear attention mechanism.

For the purpose of developing new drugs and repositioning existing ones, accurate drug-target affini...

A Multi-objective transfer learning framework for time series forecasting with Concept Echo State Networks.

This paper introduces a novel transfer learning framework for time series forecasting that uses Conc...

Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.

OBJECTIVES: To investigate the intra- and inter-rater reliability of the total methodological radiom...

A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification.

Skin cancer is frequently occurring and has become a major contributor to both cancer incidence and ...

A skin disease classification model based on multi scale combined efficient channel attention module.

Skin diseases, a significant category in the medical field, have always been challenging to diagnose...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have ex...

Multi-task learning for automated contouring and dose prediction in radiotherapy.

. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve th...

Multi-agent deep reinforcement learning-based robotic arm assembly research.

Due to the complexity and variability of application scenarios and the increasing demands for assemb...

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whet...

MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings.

Increasing evidences have indicated that circular RNAs play a crucial role in the onset and progress...

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) an...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading...

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

The purpose of this study was to develop a robust deep learning approach trained with a smallMRI dat...

Prior knowledge-based multi-task learning network for pulmonary nodule classification.

The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for...

Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A BMI-Specific approach.

PURPOSE: In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-...

Graph and Multi-Level Sequence Fusion Learning for Predicting the Molecular Activity of BACE-1 Inhibitors.

The development of BACE-1 (β-site amyloid precursor protein cleaving enzyme 1) inhibitors is a cruci...

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.

Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and ...

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

While numerous studies strive to exploit the complementary potential of MRI and PET using learning-b...

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a s...

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