Sepsis

Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients.

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant recipients, who often have weaker immune responses. The purpose of this study was to compare deep learning architectures for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine responses 12 months postvaccination in t...

Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP.

Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color-encoded multiplex hydrogel digital loop-mediated isothermal amplification (LAMP) system. The quenching of unincorporated amplification signal reporte...

PREDICTING IN-HOSPITAL MORTALITY IN CRITICAL ORTHOPEDIC TRAUMA PATIENTS WITH SEPSIS USING MACHINE LEARNING MODELS.

Purpose: This study aims to establish and validate machine learning-based models to predict death in hospital among critical orthopedic trauma patient...

Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in p...

A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy.

Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal stra...

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 against a broad-spectrum of bacterial infection. Recen...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural n...

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 to predict which phages infect a focal set of bact...

Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in dairy cattle in Florida: A 10-year retrospective study.

Supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine...

Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.

BACKGROUND: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progressi...

Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.

PURPOSE: Deep convolutional neural networks (CNN) hold promise for assisting the interpretation of dopamine transporter (DAT)-SPECT. For improved comm...

Machine Learning Models as Early Warning Systems for Neonatal Infection.

Neonatal infections pose a significant threat to the health of newborns. Associated morbidity and mortality risks underscore the urgency of prompt dia...

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 with prolonged or late-onset debilitating sequelae o...

Mining biology for antibiotic discovery.

The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence ha...

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 transplantation for keratoconus. The study include...

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 leading causes of morbidity and mortality. Revasculari...

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 significant global health challenge, annually affecting ...

Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning-Based Predictive Model.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently...

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, represents a continuum that may escalate from mild s...

Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review.

BACKGROUND: Blood-related infections are a significant concern in healthcare. They can lead to serious medical complications and even death if not pro...