Critical Care

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

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Subcategories: Sepsis
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MRI-based risk factors for intensive care unit admissions in acute neck infections.

OBJECTIVES: We assessed risk factors and developed a score to predict intensive care unit (ICU) admi...

DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation.

The precise and efficient design of potential drug molecules with diverse physicochemical properties...

DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.

BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and ge...

LMFE: A Novel Method for Predicting Plant LncRNA Based on Multi-Feature Fusion and Ensemble Learning.

: Long non-coding RNAs (lncRNAs) play a crucial regulatory role in plant trait expression and diseas...

Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes,...

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. W...

A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.

The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscor...

Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that c...

MuSIA: Exploiting multi-source information fusion with abnormal activations for out-of-distribution detection.

In the open world, out-of-distribution (OOD) detection is crucial to ensure the reliability and robu...

Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks.

Sleep apnea diagnosis relies on polysomnography (PSG), which is resource-intensive and requires manu...

Pathophysiological mechanisms of exertional dyspnea in people with cardiopulmonary disease: Recent advances.

Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, the...

Multi-head ensemble of smoothed classifiers for certified robustness.

Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ...

Prognostic value of SAPS II score for 28-day mortality in ICU patients with acute pulmonary embolism.

BACKGROUND: Acute pulmonary embolism (APE) is a common and life-threatening emergency in intensive c...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantl...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, ha...

Adaptive bigraph-based multi-view unsupervised dimensionality reduction.

As a crucial machine learning technology, graph-based multi-view unsupervised dimensionality reducti...

Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.

In the field of treatment and prevention of immune-related bone diseases, significant challenges per...

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