Sepsis

Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.

BACKGROUND: Postoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoing major surgery. Machine learning (ML) algorithms are increasingly being used in medicine, but the predictive role of logistic regression (LR) and ML algorithms for POI in high-risk populations remains unclear.

Comparing large language models for antibiotic prescribing in different clinical scenarios: which performs better?

OBJECTIVES: Large language models (LLMs) show promise in clinical decision-making, but comparative evaluations of their antibiotic prescribing accuracy are limited. This study assesses the performance of various LLMs in recommending antibiotic treatments across diverse clinical scenarios.

Estimating Periodontal Stability Using Computer Vision.

Periodontitis is a severe infection affecting oral and systemic health and is traditionally diagnosed through clinical probing-a process that is time-...

Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.

OBJECTIVE: To provide fully automatic scanner-independent 5-level categorization of the [I]FP-CIT uptake in striatal subregions in dopamine transporte...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threa...

Interpretable deep learning for deconvolutional analysis of neural signals.

The widespread adoption of deep learning to model neural activity often relies on "black-box" approaches that lack an interpretable connection between...

Hierarchical agent transformer network for COVID-19 infection segmentation.

Accurate and timely segmentation of COVID-19 infection regions is critical for effective diagnosis and treatment. While convolutional neural networks ...

Neuronal and therapeutic perspectives on empathic pain: A rational insight.

Empathy is the capacity to experience and understand the feelings of others, thereby playing a key role in a person's mental well-being essentially by...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditiona...

Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors.

BACKGROUND: Periprosthetic joint infection leads to significant morbidity and mortality after total knee arthroplasty. Preoperative and perioperative ...

Layer-by-Layer Self-Assembled Honeycomb Structure Flexible Pressure Sensor Array for Gait Analysis and Motion Posture Recognition with the Assistance of the ResNet-50 Neural Network.

With the rapid emergence of flexible electronics, flexible pressure sensors are of importance in various fields. In this study, a dopamine-modified me...

Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.

INTRODUCTION: Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfuncti...

Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A Promising Route to the Essential Molecular Descriptors.

Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we invest...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen...

Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.

Approximately 15% of patients suspected of having Parkinson's disease (PD) present dopamine active transporter (DaT) scans without evidence of dopamin...

Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning.

Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study a...

An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.

BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potent...

Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.

BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to deve...

Measuring the Level of Aflatoxin Infection in Pistachio Nuts by Applying Machine Learning Techniques to Hyperspectral Images.

This paper investigates the use of machine learning techniques on hyperspectral images of pistachios to detect and classify different levels of aflato...

Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.

This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory se...