Critical Care

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

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An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations.

Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and im...

Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine.

Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many r...

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early d...

Information-controlled graph convolutional network for multi-view semi-supervised classification.

Graph convolutional networks have achieved remarkable success in the field of multi-view learning. U...

TIMAR: Transition-informed representation for sample-efficient multi-agent reinforcement learning.

In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multipl...

Characteristics and outcomes of pulmonary barotrauma in patients with COVID-19 ARDS: A retrospective observational study.

INTRODUCTION: Pulmonary barotrauma in coronavirus disease-2019 (COVID-19) acute respiratory distress...

Assessment of Craniofacial Growth Pattern Relative to Respiratory Mandibular Movement and Sleep Characteristics: A Pilot Study.

OBJECTIVES:  The primary objective was to evaluate the influence of sagittal skeletal pattern on man...

Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach.

This work presents a robust strategy for quantifying overlapping electrochemical signatures originat...

Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Deep learning methods have been widely used for various glioma predictions. However, they are usuall...

Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation.

Manual annotation of ultrasound images relies on expert knowledge and requires significant time and ...

Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients.

BACKGROUND AND OBJECTIVE: Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive resp...

Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches.

Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long...

Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.

BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosi...

QTypeMix: Enhancing multi-agent cooperative strategies through heterogeneous and homogeneous value decomposition.

In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to coop...

EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has be...

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent ...

Fluorescence excitation-emission matrix spectroscopy combined with machine learning for the classification of viruses for respiratory infections.

Significant efforts were currently being made worldwide to develop a tool capable of distinguishing ...

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