Sepsis

A Single-Center Evaluation of Extended Infusion Piperacillin/Tazobactam for Empiric Treatment in the Intensive Care Unit.

Piperacillin/tazobactam (PTZ) extended infusion (EI) is often used empirically in the intensive car...

Development and validation of prognosis model of mortality risk in patients with COVID-19.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning ...

Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.

BACKGROUND: Detecting bacteremia among surgical in-patients is more obscure than other patients due ...

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice.

Artificial neural networks are the main tools for data mining and were inspired by the human brain a...

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

This study employed deep-learning convolutional neural networks to stage lung disease severity of Co...

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections.

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patient...

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.

Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsi...

Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images.

PURPOSE: Subretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate ...

Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study.

Missing observations are always a challenging problem that we have to deal with in diseases that req...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G pro...

Time-resolved neurotransmitter detection in mouse brain tissue using an artificial intelligence-nanogap.

The analysis of neurotransmitters in the brain helps to understand brain functions and diagnose Park...

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram.

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes dire...

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.

The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, h...

Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data.

Hepatitis C virus (HCV) remains a significant public health challenge with approximately half of the...

Stray energy transfer in single-incision robotic surgery.

INTRODUCTION: Stray energy transfer from surgical monopolar radiofrequency energy instruments can ca...

Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals.

Electroencephalogram (EEG) signals accumulate the brain's spiking activities using standardized elec...

Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients.

Baricitinib is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid a...

Positron emission tomography imaging in cardiovascular disease.

Positron emission tomography (PET) imaging is useful in cardiovascular disease across several areas,...

Using deep learning and Twitter data to identify outpatient antibiotic misuse.

The application of machine learning techniques to innovative antibacterial discovery and development.

INTRODUCTION: After the initial wave of antibiotic discovery, few novel classes of antibiotics have ...

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemi...