Sepsis

Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).

BACKGROUND: Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the m...

Transportability and Implementation Challenges of Early Warning Scores for Septic Shock in the ICU: A Perspective on the TREWScore.

The increased use of electronic health records (EHRs) has improved the availability of routine care data for medical research. Combined with machine learning techniques this has spurred the development of early warning scores (EWSs) in hospitals worldwide. EWSs are commonly used in the hospital where they have been developed, yet few have been transported to external settings and/or internationall...

Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models.

Coronavirus Disease 2019 (COVID-19) is spreading across the world, and vaccinations are running parallel. Coronavirus has mutated into a triple-mutate...

Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation.

The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions re...

Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences.

A majority of microbial infections are associated with biofilms. Targeting biofilms is considered an effective strategy to limit microbial virulence w...

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury.

This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicate...

Research Note: Study on the antibacterial activity of Chinese herbal medicine against Aspergillus flavus and Aspergillus fumigatus of duck origin in laying hens.

Aspergillus flavus and Aspergillus fumigatus were derived and identified from the ducks infected with fungi. In order to investigate the effectiveness...

Deep learning via LSTM models for COVID-19 infection forecasting in India.

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathe...

A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues d...

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification.

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist d...

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantia...

Informing deep neural networks by multiscale principles of neuromodulatory systems.

Our brains have evolved the ability to configure and adapt their processing states to match the unique challenges of acting and learning in diverse en...

ICEO, a biological ontology for representing and analyzing bacterial integrative and conjugative elements.

Bacterial integrative and conjugative elements (ICEs) are highly modular mobile genetic elements critical to the horizontal transfer of antibiotic res...

Scale-Adaptive Deep Model for Bacterial Raman Spectra Identification.

The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pat...

Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

BACKGROUND: Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Prev...

PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients.

Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for cortic...

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning.

Early use of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial re...

Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network.

Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the pr...

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution.

COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the durati...

Technologies bringing young Zebrafish from a niche field to the limelight.

Fundamental life science and pharmaceutical research are continually striving to provide physiologically relevant context for their biological studies...