Critical Care

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

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Understanding the Manufacturing Process of Lipid Nanoparticles for mRNA Delivery Using Machine Learning.

Lipid nanoparticles (LNPs), used for mRNA vaccines against severe acute respiratory syndrome coronav...

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as...

Pentagon Found Daily, Metagenomic Detection of Novel Bioaerosol Threats to Be Cost-Prohibitive: Can Virtualization and AI Make It Cost-Effective?

In 2022, the Pentagon Force Protection Agency found threat agnostic detection of novel bioaerosol th...

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer.

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric ca...

Airway leakage due to malpositioning of esophageal temperature probe during robot-assisted radical prostatectomy: a case report.

Measuring patients' core body temperature during surgery is essential and commonly performed with an...

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infect...

Antihypertensive prescribing patterns in non-dialysis dependent chronic kidney disease: Findings from the Salford Kidney Study.

BACKGROUND: Hypertension is commonly observed in patients living with chronic kidney disease (CKD). ...

Identification of Elderly Patients with Lower Respiratory Tract Infection by Artificial Intelligence Analysis of Cough Pattern Sounds.

BACKGROUND: Automatic recognition of cough sounds shows promise in the diagnosis of respiratory cond...

[Intelligent rehabilitation platform in intensive care unit].

As the development of rehabilitation medicine and critical care medicine, intensive care rehabilitat...

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning.

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing...

Multi-modality imaging in aortic stenosis: an EACVI clinical consensus document.

In this EACVI clinical scientific update, we will explore the current use of multi-modality imaging ...

dm-GAN: Distributed multi-latent code inversion enhanced GAN for fast and accurate breast X-ray image automatic generation.

Breast cancer seriously threatens women's physical and mental health. Mammography is one of the most...

Assessment of Biomedical Waste Generation in Dialysis Units: A Prospective Observational Study-Is it Time for "Green Dialysis"?

: Chronic kidney disease and as a consequence end-stage kidney disease (EKSD) is increasing globally...

Optimal design of triangular side orifice using multi-objective optimization NSGA-II.

Triangular orifices are widely used in industrial and engineering applications, including fluid mete...

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted t...

ExpertNet: A Deep Learning Approach to Combined Risk Modeling and Subtyping in Intensive Care Units.

Risk models play a crucial role in disease prevention, particularly in intensive care units (ICUs). ...

Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers.

Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, c...

Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence model.

BACKGROUND: Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) that is a...

MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference.

Development of robust and effective strategies for synthesizing new compounds, drug targeting and co...

Multimodal deep learning approaches for single-cell multi-omics data integration.

Integrating single-cell multi-omics data is a challenging task that has led to new insights into com...

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