Intensivists

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning.

With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain's activity or blood pressure, change over time....

Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.

OBJECTIVE: Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients wi...

Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.

PURPOSE OF REVIEW: Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associate...

Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features.

OBJECTIVES: Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to deploy soft-computing and ...

A Case of Dapsone-induced Mild Methemoglobinemia with Dyspnea and Cyanosis.

Dear Editor, Dapsone is a dual-function drug with antimicrobial and antiprotozoal effects and anti-inflammatory features (1). In dermatology, it is a ...

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.

BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status...

Deep transfer learning for video-based detection of newborn presence in incubator.

Preterm newborns are prone to late-onset sepsis, leading to a high risk of mortality. Video-based analysis of motion is a promising non-invasive appro...

Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach.

Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensiv...

Blood Lactate Concentration Prediction in Critical Care.

Blood lactate concentration is a reliable risk indicator of deterioration in critical care requiring frequent blood sampling. However, lactate measure...

Comparative outcomes of robot-assisted minimally invasive versus open esophagectomy in patients with esophageal squamous cell carcinoma: a propensity score-weighted analysis.

Robots are increasingly used in minimally invasive surgery. We evaluated the clinical benefits of robot-assisted minimally invasive esophagectomy (RAM...

Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.

BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, ...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well...

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large p...

Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?

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

OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic heal...

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 injury in mechanically ventilated patients with a...

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 predict severe sepsis and septic shock (Early Warning S...

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 shock and evaluate the impact on clinical practice ...

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 several recent studies. The Stewart's concept on acid-b...

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 availability for patients who urgently need it. Co...