Critical Care

Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model.

Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problem...

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cogniti...

A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are usef...

Effective allocation of resources in water pollution treatment alternatives: a multi-stage gray group decision-making method based on hesitant fuzzy linguistic term sets.

With the significant economic shift, water pollution treatment has gradually become a key problem wh...

Y-Site Compatibility of Intravenous Levetiracetam With Commonly Used Critical Care Medications.

Levetiracetam is an antiepileptic medication commonly used in critical care areas for seizure treat...

Clinical management of sepsis can be improved by artificial intelligence: yes.

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and fol...

Evaluation of Amikacin Pharmacokinetics in Critically Ill Patients with Intra-abdominal Sepsis.

Although the current widespread use of amikacin is in intra-abdominal sepsis treatment, its pharmac...

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression.

A key feature of living tissues is their capacity to remodel and grow in response to environmental c...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of ch...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke surv...

Discriminative margin-sensitive autoencoder for collective multi-view disease analysis.

Medical prediction is always collectively determined based on bioimages collected from different sou...

Multi-view ensemble learning with empirical kernel for heart failure mortality prediction.

Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's n...

Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning.

UNLABELLED: Non-contact vital sign monitoring enables the estimation of vital signs, such as heart r...

Usefulness of presepsin as diagnostic and prognostic marker of sepsis in daily clinical practice.

INTRODUCTION: Sepsis represents a major cause of morbidity and mortality in critically ill patients....

Verification with the utility of an established rapid assessment of brain safety for newly developed vaccines.

In the twenty-first century, high contagious infectious diseases such as SARS (Severe Acute Respirat...

Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial.

Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. Wit...

Machine learning-based dynamic mortality prediction after traumatic brain injury.

Our aim was to create simple and largely scalable machine learning-based algorithms that could predi...

A scalable multi-signal approach for the parallelization of self-organizing neural networks.

Self-Organizing Neural Networks (SONNs) have a wide range of applications with massive computational...

Atrial scar quantification via multi-scale CNN in the graph-cuts framework.

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternati...

Predicting O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neu...