Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Showing 2206-2226 of 6,181 articles
A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.

Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identifi...

An Improved DSA-Based Approach for Multi-AUV Cooperative Search.

Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a resea...

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential h...

Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning.

The conciliation of multiple single-disease guidelines for comorbid patients entails solving potenti...

Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.

Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predi...

Machine learning in critical care: state-of-the-art and a sepsis case study.

BACKGROUND: Like other scientific fields, such as cosmology, high-energy physics, or even the life s...

Vitamin D in the ICU: More sun for critically ill adult patients?

Critical illness in patients is characterized by systemic inflammation and oxidative stress. Vitamin...

Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.

For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular mag...

Optimal intensive care outcome prediction over time using machine learning.

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the inte...

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.

As the collection of mobile health data becomes pervasive, missing data can make large portions of d...

Automatic brain labeling via multi-atlas guided fully convolutional networks.

Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burden...

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.

Recently, dense connections have attracted substantial attention in computer vision because they fac...

Low vitamin D at ICU admission is associated with cancer, infections, acute respiratory insufficiency, and liver failure.

OBJECTIVES: Vitamin D deficiency may be associated with comorbidities and poor prognosis. However, t...

The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care.

Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals, b...

Big data and targeted machine learning in action to assist medical decision in the ICU.

Historically, personalised medicine has been synonymous with pharmacogenomics and oncology. We argue...

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG sign...

Analysis of platelet-activating factors in severe sepsis by flow cytometry and its correlation with clinical sepsis scoring system: A pilot study.

BACKGROUND: Sepsis is a major global healthcare concern. Platelets and leucocytes play a key role in...

Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia.

: Early and appropriate empiric antibiotic treatment of patients suspected of having sepsis is assoc...

Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks.

Missing data is a ubiquitous problem. It is especially challenging in medical settings because many ...

Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension.

Video-based facial expression recognition has received substantial attention over the past decade, w...

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