Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
Showing 22-42 of 9,027 articles
Structure-based virtual screening, molecular docking, and MD simulation studies: An in-silico approach for identifying potential MBL inhibitors.

The global rise of antibiotic-resistant infections has been driven in part by the spread of bacteria...

Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.

The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients,...

Non-Invasive Bedside Approaches for Assessing Microvascular Dysfunction.

Microvascular dysfunction is implicated in a range of acute and chronic conditions, ranging from car...

Single-cell image-based screens identify host regulators of Ebola virus infection dynamics.

Filoviruses such as Ebola virus (EBOV) give rise to frequent epidemics with high case fatality rates...

Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.

BACKGROUND: In the Intensive Care Unit (ICU), data stored in patient data management systems (PDMS) ...

Artificially intelligent nasal perception for rapid sepsis diagnostics.

Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due...

Unveiling the Role of Wetland Strategies in Antibiotic Risk Reduction across China by Machine Learning.

Pervasive antibiotic pollution in water environments has emerged as a serious threat to global ecosy...

Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.

Delirium is a severe and common complication among critically ill patients, particularly those with ...

Radiomics analysis for the early diagnosis of common sexually transmitted infections and skin lesions.

Early identification of sexually transmitted infection (STI) symptoms can prevent subsequent complic...

Development and validation of a nomogram model to predict postoperative delirium after resection of esophageal cancer.

The study aimed to establish and validate a nomogram model to predict postoperative delirium (POD) a...

Leveraging Multi-Level Biomarkers Using Machine Learning: Identifying Physiological and Skin Microbial Dynamics in Bd-Resistant Amphibians.

Amphibians worldwide are declining due to various anthropogenic and environmental stressors. One of ...

SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction.

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis...

Activated TBK1 promotes ACSL1-mediated microglia lipid droplet accumulation and neuroinflammation in Parkinson's disease.

UNLABELLED: Microglia-mediated neuroinflammation plays a crucial role in the progression of Parkinso...

Discovery of protein lactylation-associated biomarkers and their potential pathogenic mechanisms in recurrent spontaneous abortion.

Protein lactylation plays a critical regulatory role in various human diseases; however, its functio...

Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite.

This research proposes a machine learning controlled method for removing the antibiotic oxytetracycl...

Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

This study aims to establish and validate prediction models based on novel machine learning (ML) alg...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and...

An artificial intelligence-based approach to identify volume status in patients with severe dengue using wearable PPG data.

Dengue shock syndrome (DSS) is a serious complication of dengue infection which occurs when critical...

Multi-component metabolite electrochemical detection and analysis based on machine learning.

Metabolic molecules are highly correlated with various physiological indicators and diseases, so it ...

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