Sepsis

Drone-based geospatial prediction modeling identifies Fasciola hepatica infection risk in the Cusco Highlands of Peru.

BACKGROUND: Fascioliasis is a neglected infectious disease affecting agricultural communities worldwide, with the Peruvian Andes among the most severely affected regions. Identifying fine-scale environmental risk patterns could support targeted surveillance and control. We aimed to develop predictive models of Fasciola hepatica infection in humans and sheep using drone-derived environmental indice...

Machine Learning Tools for Predicting Pediatric Urinary Tract Infections Caused by ESBL-producing Bacteria.

BACKGROUND: The prevalence of pediatric urinary tract infections (UTIs) caused by extended-spectrum β-lactamases (ESBL)-producing bacteria is increasing worldwide and is difficult to predict. As these infections require special antibiotic treatment, which is often not started empirically, they are associated with higher rates of intensive care unit admission, morbidity and prolonged hospitalizatio...

Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.

To synthesize and critically appraise applications of machine learning (ML) in pediatric cardiac intensive care, focusing on algorithm performance, va...

Artificial intelligence for sepsis management in the emergency department.

Differential Neural Substrates of Freezing of Gait in Cerebral Small Vessel Disease and Parkinson Disease: A Dual-tracer PET Study.

PURPOSE: Freezing of gait (FOG) occurs in both Parkinson disease (PD) and cerebral small vessel disease (CSVD), but its mechanism in CSVD is unknown. ...

Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.

PURPOSE OF REVIEW: Maternal morbidity and mortality remain largely preventable, yet current risk-assessment tools identify only a fraction of women wh...

Machine learning for hemodynamic instability prediction and hemorrhage management in trauma and perioperative care.

PURPOSE OF REVIEW: Hemodynamic instability and uncontrolled hemorrhage remain leading causes of preventable morbidity and mortality in trauma and peri...

Comparison and validation of multiple machine learning algorithms for predicting MDRO infection in catheter-related bloodstream patients: a multicenter cohort study.

UNLABELLED: Early identification of patients at high risk for multidrug-resistant organism (MDRO) infection in catheter-related bloodstream infection ...

Strategies for Safer Cefepime Use to Prevent Neurotoxicity Using the Electronic Health Record.

Cefepime, a cornerstone antibiotic in critical care, is associated with underrecognized cefepime-induced neurotoxicity (CIN), particularly in patients...

De novo identification of potent ingredients for proteasome activation in MT101-5 using an AI-driven approach.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the intracellular accumulation of α-synuclein (α-syn) protein within Lewy bo...

Practical machine learning model for early and accurate prediction of disseminated intravascular coagulation before its progression to an overt stage.

BACKGROUND AND AIMS: In patients with sepsis, anticoagulant therapy is expected to have maximal efficacy when administered before the development of s...

Validation of intensive care unit predictive scoring systems in West Africans.

Intensive care unit(ICU) predictive scoring systems (PSS) are valuable in predicting outcomes in the ICU. However, they have not been validated in man...

From clinics to the environment: A systematic review of machine learning and MALDI-TOF MS in the identification of antimicrobial-resistant bacteria.

As a result of the increasing prevalence of Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) in both community and hospital s...

Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study.

OBJECTIVE: This study aimed to develop and validate stratified machine learning models for early prediction of anti-tuberculosis drug-induced liver in...

Comparing Generative Artificial Intelligence vs Experts for Detection of Catheter-Associated Urinary Tract Infection.

A retrospective cohort study compared generative artificial intelligence (GenAI) versus infection control expert for catheter-associated urinary tract...

Spatiotemporal dynamics and multiple driving factors of antimicrobial resistance in China during the COVID-19 pandemic (2019-2023): a provincial panel data analysis.

Antimicrobial resistance (AMR) poses a critical and growing global health threat, directly causing millions of deaths, with China bearing a significan...

Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients.

PURPOSE OF REVIEW: Post-traumatic care is evolving from a reactive, protocol-driven paradigm to a predictive, personalized approach. This review exami...

[Imaging of eosinophilic pneumonia : Differential diagnosis in radiological practice].

CLINICAL/METHODICAL ISSUE: Eosinophilic pneumonias are rare inflammatory lung diseases with heterogeneous clinical presentation and variable computer ...

Plasma metabolomic signatures in patients with multidrug-resistant bacterial sepsis.

BACKGROUND AND OBJECTIVE: Multidrug-resistant (MDR) bacterial infections are a leading cause of sepsis-related death. A rapid method to identify patie...

Advances in the postoperative care of the liver transplant recipient.

PURPOSE OF REVIEW: Survival rates following liver transplantation now exceed 90% at one year. However, the patient group undergoing liver transplantat...