Sepsis

Sequential classification system for recognition of malaria infection using peripheral blood cell images.

AIMS: Morphological recognition of red blood cells infected with malaria parasites is an important t...

Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the mo...

Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments.

The aim of eXplainable Artificial Intelligence (XAI) is to design intelligent systems that can expla...

Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy?

BACKGROUND: Surgical site infections are a major driver of morbidity and increased costs in the post...

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce...

Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underre...

Artificial intelligence in health care: accountability and safety.

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinic...

Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.

Recently, deep reinforcement learning, associated with medical big data generated and collected from...

Ookinete-Specific Genes and 18S SSU rRNA Evidenced in Selection and Adaptation by Sympatric Vectors.

In the southern Pacific coast of Chiapas, Mexico (SM), the two most abundant vector species, and , ...

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the...

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction.

Antimicrobial resistance has become one of the most important health problems and global action plan...

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients.

In the last few years, the importance of measuring gait characteristics has increased tenfold due to...

Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches.

Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progre...

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies.

BACKGROUND: Machine learning (ML) is increasingly being used in many areas of health care. Its use i...

Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii.

OBJECTIVES: Increased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced c...

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

Limited therapy options due to antibiotic resistance underscore the need for optimization of current...

Modeling uncertainty-seeking behavior mediated by cholinergic influence on dopamine.

Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projec...

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Achieving glycemic control in critical care patients is of paramount importance, and has been linke...

Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions.

OBJECTIVES: Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and ...

Clinical management of sepsis can be improved by artificial intelligence: no.

Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.

OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal cont...