Critical Care

Sepsis

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

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Showing 2059-2079 of 7,148 articles
Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

In the last decade essential oils have attracted scientists with a constant increase rate of more th...

May 2020 32466318
The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care U...

May 2020 32485555
Rehabilitation of older people with Parkinson's disease: an innovative protocol for RCT study to evaluate the potential of robotic-based technologies.

BACKGROUND: Parkinson's disease is one of the most frequent causes of disability among the older adu...

May 2020 32404132
Differentiation of Cytopathic Effects (CPE) induced by influenza virus infection using deep Convolutional Neural Networks (CNN).

Cell culture remains as the golden standard for primary isolation of viruses in clinical specimens. ...

May 2020 32401790
Predicting COVID-19 in China Using Hybrid AI Model.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being contro...

May 2020 32396126
Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia.

This study quantified eight small-molecule neurotransmitters collected simultaneously from prefronta...

Apr 2020 32347157
Implementation of an Artificial Intelligence Algorithm for sepsis detection.

OBJECTIVES: to present the nurses' experience with technological tools to support the early identifi...

Apr 2020 32294705
Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks.

Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively appl...

Apr 2020 32311656
Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments.

The aim of eXplainable Artificial Intelligence (XAI) is to design intelligent systems that can expla...

Mar 2020 32156641
A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce...

(1)
Mar 2020 32132525
Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underre...

Mar 2020 32308875
Artificial intelligence in health care: accountability and safety.

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinic...

Feb 2020 32284648
Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.

Recently, deep reinforcement learning, associated with medical big data generated and collected from...

(1)
Feb 2020 32184978
Ookinete-Specific Genes and 18S SSU rRNA Evidenced in Selection and Adaptation by Sympatric Vectors.

In the southern Pacific coast of Chiapas, Mexico (SM), the two most abundant vector species, and , ...

(2)
Feb 2020 32153625
Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction.

Antimicrobial resistance has become one of the most important health problems and global action plan...

Feb 2020 32498998
Early detection of sepsis utilizing deep learning on electronic health record event sequences.

BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the...

Feb 2020 32498999
Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches.

Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progre...

Feb 2020 32060344
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