Intensivists

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.

TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation.

Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease diagnosis. It is still an open problem due to some difficulties, such as a large variety of object sizes and ill-defined borders. In this paper, we present a TSU-net network that grips deeper features and captures targets of different sizes with mult...

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but interm...

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the necessary lifesaving facilities like ICU care and vent...

Efficacy and safety of an open lung ventilation strategy with staircase recruitment followed by comparison on two different modes of ventilation, in moderate ARDS in cirrhosis: A pilot randomized trial.

BACKGROUND: Mechanical ventilation in cirrhosis with acute respiratory distress syndrome (ARDS) is not widely studied. We aimed to study the effect of...

Robot assisted minimally invasive esophagectomy: safety, perioperative morbidity and short-term oncological outcome-a single institution experience.

Robot assisted minimally invasive esophagectomy (RAMIE) has evolved over the past decade to become procedure of choice at many centers all over the wo...

Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface.

Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. The...

Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pi...

One-step-immunoassay of procalcitonin enables rapid and accurate diagnosis of bacterial infection.

Procalcitonin (PCT) ( a precursor of calcitonin) attracts much attention as a reliable biomarker of bacterial infections because its concentration inc...

Error Fusion of Hybrid Neural Networks for Mechanical Condition Dynamic Prediction.

It is important for equipment to operate safely and reliably so that the working state of mechanical parts pushes forward an immense influence. Theref...

Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.

PURPOSE: Due to the increasing demand for intensive care unit (ICU) treatment, and to improve quality and efficiency of care, there is a need for adeq...

Fine-tuning of a generative neural network for designing multi-target compounds.

Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical ...

Kinematics approach with neural networks for early detection of sepsis (KANNEDS).

BACKGROUND: Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes...

Hidden Fluids in Plain Sight: Identifying Intravenous Medication Classes as Contributors to Intensive Care Unit Fluid Intake.

Fluid stewardship targets optimal fluid management to improve patient outcomes. Intravenous (IV) medications, flushes, and blood products, collective...

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analy...

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherap...

A predictive framework in healthcare: Case study on cardiac arrest prediction.

Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the ap...

Prediction of weaning from mechanical ventilation using Convolutional Neural Networks.

Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube...

Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques.

INTRODUCTION: Early diagnosis and antibiotic administration are essential for reducing sepsis morbidity and mortality; however, diagnosis remains diff...

Can artificial intelligence and machine learning help us treat sepsis?