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Intensive Care Units

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Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients.

Journal of biomedical informatics
Robust and rabid mortality prediction is crucial in intensive care units because it is considered one of the critical steps for treating patients with serious conditions. Combining mortality prediction with the length of stay (LoS) prediction adds an...

Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features.

IEEE journal of biomedical and health informatics
Sepsis is a systemic inflammatory response caused by pathogens such as bacteria. Because its pathogenesis is not clear, the clinical manifestations of patients vary greatly, and the alarming incidence and mortality pose a great threat to patients and...

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality ...

Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.

BMC pulmonary medicine
BACKGROUND: Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accur...

Prediction algorithm for ICU mortality and length of stay using machine learning.

Scientific reports
Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length...

Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort.

Annals of hematology
Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (...

External validation of a machine learning model to predict hemodynamic instability in intensive care unit.

Critical care (London, England)
BACKGROUND: Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a mul...

Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit.

Journal of critical care
PURPOSE: The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using the trends of four easy-to-collect vital signs.

Artificial intelligence (AI) versus expert: A comparison of left ventricular outflow tract velocity time integral (LVOT-VTI) assessment between ICU doctors and an AI tool.

Journal of applied clinical medical physics
PURPOSE: The application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular out...

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.

BMC medical informatics and decision making
BACKGROUND: A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared wit...