Critical Care

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

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Subcategories: Sepsis
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Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.

IMPORTANCE: Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant...

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across ...

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays imag...

Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a compl...

Safety-driven design of machine learning for sepsis treatment.

Machine learning (ML) has the potential to bring significant clinical benefits. However, there are p...

A deep convolutional neural network to simultaneously localize and recognize waste types in images.

Accurate waste classification is key to successful waste management. However, most current studies h...

Full-length ribosome density prediction by a multi-input and multi-output model.

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eu...

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image class...

Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.

Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflo...

Predicting treatment response from longitudinal images using multi-task deep learning.

Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imagi...

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.

BACKGROUND: Survival and drug response are two highly emphasized clinical outcomes in cancer researc...

A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydro...

IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control.

Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-intersection traffic...

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling.

In the increasingly pressing context of improving recycling, optical technologies present a broad po...

Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.

Jump locomotion is the basic movement of human. However, no thorough research on the recognition of ...

Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases.

Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout...

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiolo...

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection...

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