Intensivists

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnos...

Enhancing sepsis management through machine learning techniques: A review.

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic...

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is...

A roadmap for multi-omics data integration using deep learning.

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data ha...

Convolutional neural network with Huffman pooling for handling data with insufficient categories: A novel method for anomaly detection and fault diagnosis.

The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can ...

[Predicting prolonged length of intensive care unit stay machine learning].

OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models ...

A Machine Learning Understanding of Sepsis.

Sepsis is a serious cause of morbidity and mortality and yet its pathophysiology remains elusive. Recently, medical and technological advances have he...

Perioperative Risk Assessment in Pancreatic Surgery Using Machine Learning.

Pancreatic surgery is associated with a high risk for postoperative complications and death of patients. Complications occur in a variable interval af...

Exploring Features Contributing to the Early Prediction of Sepsis Using Machine Learning.

The increasing availability of electronic health records and administrative data and the adoption of computer-based technologies in healthcare have si...

Autonomous Systems and Artificial Intelligence - Hype or Prerequisite for P5 Medicine?

For meeting the challenge of aging, multi-diseased societies, cost containment, workforce development and consumerism by improved care quality and pat...

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose o...

Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils.

Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic...

Towards real-time diagnosis for pediatric sepsis using graph neural network and ensemble methods.

OBJECTIVE: The rapid onset of pediatric sepsis and the short optimal time for resuscitation pose a severe threat to children's health in the ICU. Time...

Predicting ventilator-associated pneumonia with machine learning.

Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifyi...

MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs.

The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to buil...

RNA-binding protein recognition based on multi-view deep feature and multi-label learning.

RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target R...

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis...

Four Biomarkers-Based Artificial Neural Network Model for Accurate Early Prediction of Bacteremia with Low-level Procalcitonin.

OBJECTIVE: Procalcitonin levels above 2.0 ng/mL are associated with a higher risk of severe sepsis. Bacteremia with procalcitonin levels lower than 2....

[A Case of Descending Colon and Rectal Cancer with Acute Myeloid Leukemia Performed Robot‒Assisted Hartmann's Procedure].

The case is a 68‒year‒old male, who had been diagnosed with acute myeloid leukemia(AML)prior to rectal cancer surgery, was referred to our hospital fo...

Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy.

OBJECTIVE: Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that ...