Sepsis

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration. Grounded in the Antimicrobial Protection Hypothesis, this study introduces a sheaf-theoretic machine learning framework, Sheaf-ML, for integrating multimodal health data and assessing infection-related cognitive risk. Sheaf-ML constructs a unified ...

From free text to SOFA score: automated reconstruction of sepsis severity from unstructured clinical notes

To evaluate the ability of a natural language processing system to automatically reconstruct the SOFA score from unstructured clinical notes in patients with sepsis and validate its applicability in intensive care units. Retrospective study in the MIMIC-III database that included 284 adults with sepsis. The SOFA calculated with structured data was compared with the SOFA reconstructed by free text ...

Pandemic-Potential Viruses are a Blind Spot for Frontier Open-Source LLMs

We study large language models (LLMs) for front-line, pre-diagnostic infectious-disease triage, a critically understudied stage in clinical interventi...

When AI Meets the FDA: An Evaluation of Large Language Models Performance in Regulatory and Clinical Trial Data Extraction, Synthesis, and Analysis

Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed in...

Machine Learning–Driven Drug Optimization for Typhoid Fever Based on Patient Profiles

Typhoid fever remains a major Global public health concern, with treatment outcomes dependent on antimicrobial resistance (AMR) and patient variabilit...

Integrating DHS/MIS Biomarkers with 34 Years of CHIRPS-NDVI Climate Data for Malaria Risk Prediction in Nigeria: A Machine Learning and Spatial Mapping Approach

The estimates of national disease risk are considerably limited by the time of conducted surveys and the geographical inadequacies in surveillance, no...

Predicting Disease Progression in Critically Ill Patients Using Frequency-Enhanced Time-series Forecasting

Accurate disease progression prediction is vital for managing critically ill patients in intensive care. Existing deep learning approaches mainly oper...

Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls

Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...

Comparison of local large language models for extraction of signs and symptoms data from electronic health records

Electronic health records (EHRs) provide a large source of data that can be used for research purposes. Extraction of information from unstructured cl...

Single-Cell Nanomotion and Machine Learning for Parallel Bacterial Identification and Antibiotic Screening

Rapid and accurate identification of bacterial infections and their resistance to antibiotics is critical to effective clinical decision-making and co...

Broad-spectrum coronavirus inhibitors discovered by modeling viral fusion dynamics.

Development of oral, broad-spectrum therapeutics targeting SARS-CoV-2, its variants, and related coronaviruses could curb the spread of COVID-19 and a...

Comparative evaluation of the accuracy and reliability of ChatGPT versions in providing information on infection.

OBJECTIVE: This study aimed to evaluate the accuracy and reliability of responses provided by three versions of ChatGPT (ChatGPT-3.5, ChatGPT-4, and C...

Construction of a predictive model for rebleeding risk in upper gastrointestinal bleeding patients based on clinical indicators such as infection.

BACKGROUND: The annual incidence of upper gastrointestinal hemorrhage (UGIB) is about 60 cases/100,000 people, and about 40% of UGIB patients have hem...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanis...

AI-driven multi-omics profiling of sepsis immunity in the digestive system.

Sepsis is a life-threatening systemic inflammatory syndrome characterized by a complex immune biphasic imbalance. Monitoring of immune status has not ...

Amphetamine use and Parkinson's disease: integration of artificial intelligence prediction, clinical corroboration, and mechanism of action analyses.

Parkinson's disease (PD) is an increasingly prevalent neurologic condition for which symptomatic, but not preventative, treatment is available. Drug r...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperati...

Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN.

Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detect...

Artificial Intelligence in Endoscopy for Predicting Helicobacter pylori Infection: A Systematic Review and Meta-Analysis.

PURPOSE: This meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI) based on endoscopy for detecting Helicobacter p...

SwinDFU-Net: Deep learning transformer network for infection identification in diabetic foot ulcer.

BACKGROUND: The identification of infection in diabetic foot ulcers (DFUs) is challenging due to variability within classes, visual similarity between...