Sepsis

Transplant nephrectomy with peritoneal window: Georgetown University experience.

OBJECTIVES: Transplant nephrectomy is a technically challenging procedure with high complication rates. Morbidity and mortality are mostly due to hemorrhage or infection and are reported to be 17-60% and 1-39%, respectively. The most common surgical technique for transplant nephrectomy is sub-capsular, extraperitoneal approach which may result in fluid accumulation and subsequent super-infection. ...

Retrospective Observational Study of the Clinical Performance Characteristics of a Machine Learning Approach to Early Sepsis Identification.

UNLABELLED: To estimate performance characteristics and impact on care processes of a machine learning, early sepsis recognition tool embedded in the electronic medical record.

Nutritional, biochemical and microbiological composition of Mart. sap in Burkina Faso.

The palmyra palm Mart. grow wild and gives natural stands in several localities of central-eastern and eastern regions of Burkina Faso. This work aim...

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform...

Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

PURPOSE: This study investigated the potential of deep convolutional neural networks (CNN) for automatic classification of FP-CIT SPECT in multi-site ...

Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making.

There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data fo...

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study.

A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic...

Predicting sepsis with a recurrent neural network using the MIMIC III database.

OBJECTIVE: Predicting sepsis onset with a recurrent neural network and performance comparison with InSight - a previously proposed algorithm for the p...

Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach.

Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a mach...

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health ...

A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes.

The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and c...

Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis.

OBJECTIVE: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderatel...

Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose.

The United States of America is fighting against one of its worst-ever drug crises. Over 900 people a week die from opioid- or heroin-related overdose...

Future of Radiotherapy in Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radio...

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the pre...

Morphology-based classification of mycobacteria-infected macrophages with convolutional neural network: reveal EsxA-induced morphologic changes indistinguishable by naked eyes.

EsxA is an essential virulence factor for Mycobacterium tuberculosis (Mtb) pathogenesis as well as an important biomarker for Mtb detection. In this s...

mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology.

Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSp...

An intelligent warning model for early prediction of cardiac arrest in sepsis patients.

BACKGROUND: Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time req...

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ...

Chronic wound assessment and infection detection method.

BACKGROUND: Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a te...