Intensivists

Endocan serum concentration in uninfected newborn infants.

INTRODUCTION: Endocan is a specific endothelial mediator involved in the inflammatory response. Its role in the diagnosis of sepsis has been studied in adult patients and late onset neonatal sepsis. The clinical signs of early onset sepsis (EOS) are nonspecific and routinely used biomarkers, such as C-reactive protein and procalcitonin, have low sensitivity, specificity and positive predictive val...

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health r...

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 ...

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...

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 ...

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.

BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised ...

Designing minimal and scalable insect-inspired multi-locomotion millirobots.

In ant colonies, collectivity enables division of labour and resources with great scalability. Beyond their intricate social behaviours, individuals o...

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...

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.

Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In ...

A machine learning approach for predicting urine output after fluid administration.

BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation.

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using e...

Refining humane endpoints in mouse models of disease by systematic review and machine learning-based endpoint definition.

Ideally, humane endpoints allow for early termination of experiments by minimizing an animal's discomfort, distress and pain, while ensuring that scie...

Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied mac...

Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems.

This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in...

Machine learning for patient risk stratification for acute respiratory distress syndrome.

BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-...

An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy.

OBJECTIVE: This study was designed to assess clinical predictors of hypoxemia and develop an artificial neural network (ANN) model for prediction of h...

Discriminative multi-source adaptation multi-feature co-regression for visual classification.

Learning an effective visual classifier from few labeled samples is a challenging problem, which has motivated the multi-source adaptation scheme in m...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecu...

Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentati...

An attention based deep learning model of clinical events in the intensive care unit.

This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocar...