Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
Showing 1702-1722 of 9,043 articles
Prognostic value of the Glucose-to-Albumin ratio in sepsis-related mortality: A retrospective ICU study.

AIMS: To investigate the prognostic value of the glucose-to-albumin ratio (GAR) in predicting 30-day...

The best machine learning algorithm for building surgical site infection predictive models: A systematic review and network meta-analysis.

BACKGROUND: Many machine learning (ML) algorithms have been used to develop surgical site infection ...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage r...

Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries.

INTRODUCTION: Antimicrobial resistance (AMR) is a global health challenge, and antimicrobial suscept...

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study.

BACKGROUND: The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue t...

Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO/NaOCl reaction pathways.

This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), a...

Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.

Human Immunodeficiency Virus (HIV) is a retrovirus that weakens the immune system, increasing vulner...

Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.

The increasing incidence of dengue virus (DENV) infections poses significant public health challenge...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays ...

ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.

BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered out...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid m...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remai...

Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.

In recent years, the incidence of refractory Mycoplasma pneumoniae pneumonia (RMPP) has significantl...

Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection.

By utilizing the synergistic effects of a dual-metal cobalt@copper electrode and advanced machine le...

Bio inspired feature selection and graph learning for sepsis risk stratification.

Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accu...

A Chemistry-Informed Generative Deep Learning Approach for Enhancing Voltammetric Neurochemical Sensing in Living Mouse Brain.

Exploring the time-resolved dynamics of neurochemicals is essential for deciphering neuronal functio...

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