Sepsis

Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures.

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics...

Predicting Clostridioides difficile infection outcomes with explainable machine learning.

BACKGROUND: Clostridioides difficile infection results in life-threatening short-term outcomes and the potential for subsequent recurrent infection. Predicting these outcomes at diagnosis, when important clinical decisions need to be made, has proven to be a difficult task.

Robot-related injuries in the workplace: An analysis of OSHA Severe Injury Reports.

Industrial robots are increasingly commonplace, but research on prototypical accidents and injuries has been sparse, hindering evidence-based safety s...

Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers.

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-...

Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and co...

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments.

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Sin...

Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes.

Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission o...

A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.

BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catechol...

Multi-Omics Analysis by Machine Learning Identified Lysophosphatidic Acid as a Biomarker and Therapeutic Target for Porcine Reproductive and Respiratory Syndrome.

As a significant infectious disease in livestock, porcine reproductive and respiratory syndrome (PRRS) imposes substantial economic losses on the swin...

Machine Learning Prediction of Small Molecule Accumulation in Enhanced with Descriptor Statistics.

Antibiotic resistance, particularly among Gram-negative bacteria, poses a significant healthcare challenge due to their ability to evade antibiotic ac...

Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of system...

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.

The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobi...

Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with...

Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation.

OBJECTIVE: Cellulitis is the most common cause of skin-related hospitalisations, and the mortality of patients with sepsis remains high. Some stratifi...

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term,...

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to ...

Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance.

UNLABELLED: The erythromycin resistance RNA methyltransferase () confers cross-resistance to all therapeutically important macrolides, lincosamides, a...

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI models. These explanations can be evaluated for (i)...

Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.

OBJECTIVES: The increased utilization of Artificial intelligence (AI) in healthcare changes practice and introduces ethical implications for AI adopti...

Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy.

Septic cardiomyopathy (SCM) is characterized by an abnormal inflammatory response and increased mortality. The role of efferocytosis in SCM is not wel...