Sepsis

Unrealistic Data Augmentation Improves the Robustness of Deep Learning-Based Classification of Dopamine Transporter SPECT Against Variability Between Sites and Between Cameras.

We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras. A CNN was trained on a homogeneous dataset comprising 1,100 I-labeled 2β-carbomethoxy-3β-(4-iodophenyl)--(3-fluoropropyl)nortropane SPECT images using strongly unrea...

From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes.

Lactate analysis plays an important role in sports science and training decisions for optimising performance, endurance, and overall success in sports. Two parameters are widely used for these goals: aerobic (AeT) and anaerobic (AnT) thresholds. However, determining AeT proves more challenging than AnT threshold due to both physiological intricacies and practical considerations. Thus, the aim of t...

Machine learning predictive modeling for condemnation risk assessment in antibiotic-free raised broilers.

The condemnation of broiler carcasses in the poultry industry is a major challenge and leads to significant financial losses and food waste. This stud...

Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection.

UNLABELLED: Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. Th...

Development and validation of a machine learning-based model to assess probability of systemic inflammatory response syndrome in patients with severe multiple traumas.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a predictor of serious infectious complications, organ failure, and death in patients wi...

Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.

Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and trad...

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic...

Using machine learning to dissect host kinases required for Leishmania internalization and development.

The Leishmania life cycle alternates between promastigotes, found in the sandfly, and amastigotes, found in mammals. When an infected sandfly bites a ...

A new hybrid learning control system for robots based on spiking neural networks.

This paper presents a new hybrid learning and control method that can tune their parameters based on reinforcement learning. In the new proposed metho...

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate...

Novel molecular inhibitor design for Plasmodium falciparum Lactate dehydrogenase enzyme using machine learning generated library of diverse compounds.

Generative machine learning models offer a novel strategy for chemogenomics and de novo drug design, allowing researchers to streamline their explorat...

Artificial intelligence and prescription of antibiotic therapy: present and future.

INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exem...

Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.

INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioide...

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of myc...

Surface enhanced Raman spectroscopy and machine learning for identification of beta-lactam antibiotics resistance gene fragment in bacterial plasmid.

BACKGROUND: Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative...

Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for b...

Prediction of sepsis mortality in ICU patients using machine learning methods.

PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is cruc...

Distinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.

Novel artificial intelligence methods can aide in identification of cases of conditions using only unstructured electronic health record data. This gr...

Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.

Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learnin...

Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy.

Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and ...