Intensivists

Respiratory parameters and acute kidney injury in acute respiratory distress syndrome: a causal inference study.

BACKGROUND: Assess the respiratory-related parameters associated with subsequent severe acute kidney...

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns...

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic s...

Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predi...

A Double-Blind, Randomized Control Trial of Rapidly Infused High Strong Ion Difference (SID) Fluid Versus Hartmann's Solution on Acid-Base Status in Sepsis Patients in the Emergency Department.

BACKGROUND: Balanced fluids are preferred in initial resuscitation of septic patients based on sever...

Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice.

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased ...

Learning Portuguese Clinical Word Embeddings: A Multi-Specialty and Multi-Institutional Corpus of Clinical Narratives Supporting a Downstream Biomedical Task.

In this paper, we trained a set of Portuguese clinical word embedding models of different granularit...

Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis.

A Multi-channel Deep Learning Approach for Segmentation of the Left Ventricular Endocardium from Cardiac Images.

Cardiac segmentation is the first most important step in assessing cardiac diseases. However, it sti...

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great signific...

Novel Automatic Epilepsy Detection Method Multi-weight Transition Network.

The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an impo...

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices.

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decad...

A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer.

Recurrence is a significant prognostic factor in patients with triple negative breast cancer, and th...

OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics.

We present a novel neural-network-based pipeline for segmentation of 3D muscle and bone structures f...

Automatic Detection of Focal Liver Lesions in Multi-phase CT Images Using A Multi-channel & Multi-scale CNN.

There are multiple types of tumors occurring in the liver, each of which have a different visual app...

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury.

Prior studies have used vital signs and laboratory measurements with conventional modeling technique...

Inverting the Turing Test - Machine Learning to Detect Cognition in the ICU.

Multi-Compliance Printing Techniques for the Fabrication of Customisable Hand Exoskeletons.

To be successful, hand exoskeletons require customisable low encumbrance design with multi-compliant...

New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.

Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.

In this paper, we developed a deep convolutional neural network (CNN) for the classification of mali...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropr...