Intensivists

[The Swecrit Biobank, associated clinical registries, and machine learning (artificial intelligence) improve critical care knowledge].

The unique Swecrit Biobank and its associated clinical registries for sepsis, ARDS, cardiac arrest, trauma, and COVID-19 include more than 150,000 blood samples and descriptions of critically ill patients. These assets provide a unique opportunity to research and improve the care of the most seriously ill patients through biomarker analyses, proteomic studies, and genetic and epigenetic studies us...

Continuous visualization and validation of pain in critically ill patients using artificial intelligence: a retrospective observational study.

Machine learning tools have demonstrated viability in visualizing pain accurately using vital sign data; however, it remains uncertain whether incorporating individual patient baselines could enhance accuracy. This study aimed to investigate improving the accuracy by incorporating deviations from baseline patient vital signs and the concurrence of the predicted artificial intelligence values with ...

MicroRNA-30a inhibits cell proliferation in a sepsis-induced acute kidney injury model by targeting the YAP-TEAD complex.

BACKGROUND: Acute kidney injury (AKI) is a primary feature of renal complications in patients with sepsis. MicroRNA (miRNA/miR)-30a is an essential re...

An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM.

Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. A...

Effects of oral melatonin premedication on hemodynamic responses to intubation, anesthetic requirements and postoperative sedation: A randomized trial.

BACKGROUND AND AIMS: Sedative effects of melatonin may have an additive effect on general anesthesia (GA). We compared hemodynamic response to intubat...

Beyond SEP-1 Compliance: Assessing the Impact of Antibiotic Overtreatment and Fluid Overload in Suspected Septic Patients.

BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) developed the Severe Sepsis and Septic Shock Performance Measure bundle (SEP-1) metri...

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt ad...

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-base...

Selection/control concurrent optimization of BLDC motors for industrial robots.

This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLD...

Using machine learning to estimate health spillover effects.

We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the mo...

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed ...

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; ho...

Propensity-matched analysis of robotic versus sternotomy approaches for mitral valve replacement.

To compare early and medium-term outcomes between robotic and sternotomy approaches for mitral valve replacement (MVR). Clinical data of 1393 cases wh...

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed ...

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit.

BACKGROUND: Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and...

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a mach...

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly re...

Pulmonary contusion: automated deep learning-based quantitative visualization.

PURPOSE: Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide earl...

FT-GAT: Graph neural network for predicting spontaneous breathing trial success in patients with mechanical ventilation.

BACKGROUND AND OBJECTIVES: Intensive care unit (ICU) physicians perform weaning procedures considering complex clinical situations and weaning protoco...

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their respective advantages in soil carbon content predictio...