Critical Care

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

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Subcategories: Sepsis
Showing 988-1008 of 7,427 articles
Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients i...

High-throughput point-of-care serum iron testing utilizing machine learning-assisted deep eutectic solvent fluorescence detection platform.

In this study, a high-throughput point-of-care testing (HT-POCT) system for detecting serum iron was...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) o...

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. C...

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images.

Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and graduall...

Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information.

Intelligent Transportation Systems (ITS) are essential for modern urban development, with urban flow...

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction.

Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend ...

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurren...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

The widespread prevalence of sleep problems in children highlights the importance of timely and accu...

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia.

Many studies have shown that infectious diseases are responsible for the majority of deaths in child...

Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.

The COVID-19 outbreak caused saturations of hospitals, highlighting the importance of early patient ...

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment ...

Data-driven explainable machine learning for personalized risk classification of myasthenic crisis.

OBJECTIVE: Myasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring int...

UMS-ODNet: Unified-scale domain adaptation mechanism driven object detection network with multi-scale attention.

Unsupervised domain adaptation techniques improve the generalization capability and performance of d...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure...

Prediction of dialysis adequacy using data-driven machine learning algorithms.

BACKGROUND: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reducti...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power...

A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network.

One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of...

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