Intensivists

I, DOCTOR: The role of machine learning in phenotyping ARDS.

Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma).

BACKGROUND: Critical care research in Canada is conducted primarily in academically-affiliated intensive care units with established research infrastructure, including research coordinators (RCs). Recently, efforts have been made to engage community hospital ICUs in research albeit with barriers. Automation or artificial intelligence (AI) could aid the performance of routine research tasks. It is ...

Development of a Novel Anesthesia Airway Management Robot.

Non-invasive positive pressure ventilation has attracted increasing attention for air management in general anesthesia. This work proposes a novel rob...

Influence of Critical Care Transport Ventilator Management on Intensive Care Unit Care.

OBJECTIVE: High tidal volume ventilation is associated with ventilator-induced lung injury. Early introduction of lung protective ventilation improves...

Adrenal Insufficiency in Septic Patients Admitted to Intensive Care Unit: Prevalence and Associated Factors.

Adrenal insufficiency (AI) is associated with an increase in the risk of mortality in ICU-admitted septic patients. It should be suspected not only i...

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning.

This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict lo...

A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis.

A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity ...

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making p...

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.

PURPOSE: Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low perce...

Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning.

BACKGROUND: Sepsis-associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution w...

An empirical study of using radiology reports and images to improve ICU-mortality prediction.

The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management for its capability of predicting important outcomes,...

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients.

Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by ...

Dynamics and Control of a Magnetic Transducer Array Using Multi-Physics Models and Artificial Neural Networks.

A linear mechanical oscillator is non-linearly coupled with an electromagnet and its driving circuit through a magnetic field. The resulting non-linea...

Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.

In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the pr...

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-...

Short-term and pathologic outcomes of robotic versus open pancreatoduodenectomy for periampullary and pancreatic head malignancy: an early experience.

Open pancreatoduodenectomy (OPD) is associated with high perioperative morbidity. Adoption of robot-assisted pancreatoduodenectomy (RAPD) has been slo...

A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation From EEG Signals.

Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. Howe...

DMC-Fusion: Deep Multi-Cascade Fusion With Classifier-Based Feature Synthesis for Medical Multi-Modal Images.

Multi-modal medical image fusion is a challenging yet important task for precision diagnosis and surgical planning in clinical practice. Although sing...

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. ...

Two-phase non-invasive multi-disease detection via sublingual region.

Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, compute...