Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
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Graphene FET biochip on PCB reinforced by machine learning for ultrasensitive parallel detection of multiple antibiotics in water.

Antibiotics like Ciprofloxacin (Cfx), tetracycline (Tet) and Tobramycin (Tob) are commonly used agai...

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge ...

Machine Learning Models as Early Warning Systems for Neonatal Infection.

Neonatal infections pose a significant threat to the health of newborns. Associated morbidity and mo...

Current update on the neurological manifestations of long COVID: more questions than answers.

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting wi...

Mining biology for antibiotic discovery.

The rise of antibiotic resistance calls for innovative solutions. The realization that biology can b...

Machine learning methods to identify risk factors for corneal graft rejection in keratoconus.

Machine learning can be used to identify risk factors associated with graft rejection after corneal ...

A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients.

Sepsis, a life-threatening condition triggered by the body's response to infection, remains a signif...

Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms.

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the lead...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, re...

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.

BACKGROUND: Current blood glucose monitoring (BGM) methods are often invasive and require repetitive...

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurren...

Critical care studies using large language models based on electronic healthcare records: A technical note.

The integration of large language models (LLMs) in clinical medicine, particularly in critical care,...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure...

Miniature Robots for Battling Bacterial Infection.

Micro/nanorobots have shown great promise for minimally invasive bacterial infection therapy. Howeve...

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