Sepsis

Can Artificial Intelligence Be Utilized to Predict Real-Time Adverse Outcomes in Individuals Arriving at the Emergency Department With Hyperglycemic Crises?: Implications for APRN Practice.

This column on translating research into practice is crafted to offer advanced practice registered nurses an analysis of current research topics that hold practical relevance for emergency care settings. The article titled "Using Artificial Intelligence to Predict Adverse Outcomes in Emergency Department Patients With Hyperglycemic Crises in Real Time," authored by C. Hsu et al. (2023), investigat...

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardi...

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the...

Safety and Efficacy of Acute Central Venous Catheters for Hemodialysis with Sodium Bicarbonate versus an Antibiotic Catheter Locking Solution.

This study was conducted to determine the safety and efficacy of acute central venous catheters (CVC) using a sodium bicarbonate catheter locking solu...

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their rel...

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial in...

Mycobactin analogue interacting with siderophore efflux-pump protein: insights from molecular dynamics simulations and whole-cell assays.

INTRODUCTION: In response to continued public health emergency of antimicrobial resistance (AMR), a significant key strategy is the discovery of novel...

RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning.

BACKGROUND: Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pha...

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, ...

A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing.

OBJECTIVE: To integrate an enhanced molecular diagnostic technique to develop and validate a machine-learning model for diagnosing sepsis.

-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

PURPOSE: This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical...

Antibiotic discovery with artificial intelligence for the treatment of infections.

UNLABELLED: Global challenges presented by multidrug-resistant infections have stimulated the development of new treatment strategies. We reported th...

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected a...

Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT.

Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical sympt...

A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.

Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs...

Data mining and machine learning in HIV infection risk research: An overview and recommendations.

In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contribu...

Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model.

Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduc...

Comprehensive Review on the Virulence Factors and Therapeutic Strategies with the Aid of Artificial Intelligence against Mucormycosis.

Mucormycosis, a rare but deadly fungal infection, was an epidemic during the COVID-19 pandemic. The rise in cases (COVID-19-associated mucormycosis, C...

Early prediction of sepsis-induced respiratory tract infection using a biomarker-based machine-learning algorithm.

Early and differential diagnosis of sepsis is essential to avoid unnecessary antibiotic use and further reduce patient morbidity and mortality. Here, ...

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning.

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand fo...