Sepsis

Exploration in neo-Hebbian reinforcement learning: Computational approaches to the exploration-exploitation balance with bio-inspired neural networks.

Recent theoretical and experimental works have connected Hebbian plasticity with the reinforcement learning (RL) paradigm, producing a class of trial-and-error learning in artificial neural networks known as neo-Hebbian plasticity. Inspired by the role of the neuromodulator dopamine in synaptic modification, neo-Hebbian RL methods extend unsupervised Hebbian learning rules with value-based modulat...

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden ...

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.

The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of m...

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition.

The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for i...

Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm.

This study was aimed at exploring the application of image segmentation based on full convolutional neural network (FCN) in liver computed tomography ...

Dietary supplementation of propolis enhanced the innate immune response against Edwardsiella piscicida challenge in turbot (Scophthalmus maximus).

Propolis is non-hazardous resinous substance mixture containing bioactive ingredients such as polyphenols, flavonoids and organic acid. It has been wi...

Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.

BACKGROUND: Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognit...

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany.

During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and inter...

Advanced medical monitoring for the battlefield: A review on clinical applicability of compensatory reserve measurements for early and accurate hemorrhage detection.

Hemorrhagic shock remains the leading cause of mortality in civilian trauma and battlefield settings. The ability of combat medics and other military ...

Profiling of 35 Cases of Hb S/Hb E (: c.20A>T/: c.79G>a), Disease and Association with α-Thalassemia and β-Globin Gene Cluster Haplotypes from Odisha, India.

Hb S/Hb E (: c.20A>T/: c.79G>A) is an uncommon variant of sickle cell disease resulting from coinheritance of Hb S and Hb E. Clinico-hematological and...

Identification of antimicrobial peptides from the human gut microbiome using deep learning.

The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational pr...

INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network.

Testing is one of the important methodologies used by various countries in order to fight against COVID-19 infection. The infection is considered as o...

Anti-Fatigue and Exercise Performance Improvement Effect of Extract in Mice.

(GT) is a native perennial plant growing across the coastline areas in Taiwan. The current study aimed to examine the efficacy of GT extract in ameli...

Anticipating antibiotic resistance.

Machine learning can use clinical history to lower the risk of infection recurrence.

Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume.

World Health Organization (WHO) declared COVID-19 (COronaVIrus Disease 2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing d...

Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm.

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not...

Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage.

Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care meas...

Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.

Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support syst...

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.

Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule...

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to ...