Sepsis

Construction of a depression risk prediction model for hepatitis B patients based on machine learning strategy.

BACKGROUND: Hepatitis B (HBV) is a chronic viral infection that can lead to cirrhosis, liver failure, and liver cancer, and has a profound impact on the patient's mental health. However, current depression screening mainly relies on self-filled scales and clinical experience, lacking objective and efficient prediction tools. This study aims to construct a risk prediction model for depression in he...

Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.

Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases.

BACKGROUND: Antimicrobial resistance (AMR) poses a global threat to public health, though AI models have shown transformative potential in combating A...

Validation of a Trained AI Imaging Model for Detecting Diabetic Foot Deformities.

Diabetes is a leading cause of morbidity and mortality, contributing to complications such as cardiovascular disease, kidney failure and lower-limb am...

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming ...

Neural Prediction Errors as a Unified Cue for Abstract Visual Reasoning.

Humans exhibit remarkable abilities in recognizing relationships and performing complex reasoning. In contrast, deep neural networks have long been cr...

A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.

OBJECTIVE: To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a ...

Simulation-based evaluation of ChatGPT for healthcare associated infection surveillance using validated case scenarios.

BACKGROUND: Surveillance for healthcare-associated infections is central to infection prevention but remains complex, resource-intensive, and variable...

Multi-omics and machine learning-based profiling of severity signatures in Mycoplasma pneumoniae infection in children.

Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory infection in children; however, the mechanisms driving its progression to severe disease...

Timing matters: a machine learning-driven comparison of community and hospital-onset sepsis.

Machine learning-driven identification of serotype-independent pneumococcal vaccine candidates using samples from human infection challenge studies.

Identifying conserved, immunogenic proteins that confer protection against Streptococcus pneumoniae (pneumococcus) colonization could enable developme...

How ready are we to use artificial intelligence in our fight against Antimicrobial resistance? An ESGAID and EAAS perspective.

INTRODUCTION: Antimicrobial resistance (AMR) remains one of the greatest threats to global health, requiring innovative approaches to antibiotic disco...

Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study.

BACKGROUND: Venous thromboembolism (VTE) is a common and severe complication in intensive care unit (ICU) patients with sepsis. Conventional risk stra...

Computational framework and machine learning approach to fractional order soil helminth infections disease model for control mechanism.

Soil-transmitted helminth (STH) infections are exceedingly common, estimated to afflict 1.5 billion individuals worldwide, or 24% of the total populat...

Early prediction of colonization by carbapenemase-producing enterobacterales at ICU admission using machine learning.

Colonization by carbapenemase-producing Enterobacterales (CPE) on admission to an intensive care unit (ICU) poses a serious threat to infection contro...

Predicting antimicrobial resistance in Staphylococcus aureus using machine learning: Insights from a five-year surveillance study.

Staphylococcus aureus is a leading cause of both community- and hospital-acquired infections, and the growing prevalence of antimicrobial resistance c...

Identification of the biomarkers associated with lipid metabolism and endoplasmic reticulum stress in pediatric sepsis through bioinformatics analysis and quantitative reverse transcriptase PCR (qRT-PCR) assay.

BACKGROUND: Sepsis constitutes a critical clinical condition characterized by organ failure and systemic inflammation, especially affecting children. ...

AI-Driven nanofiber platforms for essential oil delivery in dry period cows: a sustainable strategy against mastitis and antimicrobial resistance.

The routine utilisation of prophylactic antibiotics in dairy cows during the dry period has been demonstrated to accelerate the rise of antimicrobial ...

A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis.

Sepsis is a complex systemic inflammatory syndrome that currently lacks stable and specific biomarkers. Multi-omics integration combined with machine ...

Artificial intelligence predicts healthcare workers' antibiotic use intentions from psychological and behavioral measures across multiple theories.

Antimicrobial resistance (AMR) remains a global health priority, partly driven by non-guideline-concordant antibiotic prescribing among healthcare wor...