Sepsis

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across...

Predicting COVID-19 in China Using Hybrid AI Model.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as tradi...

Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video).

BACKGROUND: Helicobacter pylori (H. pylori) eradication is required to reduce incidence related to gastric cancer. Recently, it was found that even af...

Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia.

This study quantified eight small-molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mice ( = 23) during wakefulne...

Implementation of an Artificial Intelligence Algorithm for sepsis detection.

OBJECTIVES: to present the nurses' experience with technological tools to support the early identification of sepsis.

Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks.

Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object...

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome.

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requ...

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 explain their predictions or recommendations to humans....

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 antibiotic overuse, and lower healthcare costs. H...

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 underreporting as current methodology relies heavily on m...

Artificial intelligence in health care: accountability and safety.

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of...

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

Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for co...

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 , were susceptible to different Pvs25/28 haplotypes...

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 plans have been proposed globally. Prevention plays a ...

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 outcome for the patient. Machine learning models ...

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 their direct relationship with various neurologic...

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 progressive lung damage and represent the primary cause ...

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 in infection management is catching up as identifie...

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 diagnostics. In some bacterial species, antimicro...

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

Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons - another neuromodulatory...