AIMC Topic: Intensive Care Units

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Real-time monitoring of air pathogens in the ICU with biosensors and robots.

Critical care (London, England) Nov 14, 2022
Biosensing Techniques Environmental Monitoring Robotics Humans Intensive Care Units
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Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan's largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development.

Anaesthesia, critical care & pain medicine Oct 24, 2022
OBJECTIVE: While clinical Artificial Intelligence (cAI) mortality prediction models and relevant studies have increased, limitations including the lack of external validation studies and inadequate model calibration leading to decreased overall accur...
Humans Deep Learning Hospital Mortality Japan Artificial Intelligence Intensive Care Units
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Dynamic prediction of life-threatening events for patients in intensive care unit.

BMC medical informatics and decision making Oct 22, 2022
BACKGROUND: Early prediction of patients' deterioration is helpful in early intervention for patients at greater risk of deterioration in Intensive Care Unit (ICU). This study aims to apply machine learning approaches to heterogeneous clinical data f...
China Adolescent Humans Intensive Care Units Machine Learning Hospitalization
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Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients.

Journal of biomedical informatics Oct 5, 2022
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...
Machine Learning Length of Stay Intensive Care Units, Neonatal Algorithms Humans Intensive Care Units Infant, Newborn
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Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features.

IEEE journal of biomedical and health informatics Aug 11, 2022
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...
Critical Care Intensive Care Units Sepsis Artificial Intelligence Machine Learning Humans
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Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.

Journal of medical Internet research Aug 9, 2022
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 ...
Humans Cohort Studies Intensive Care Units Heart Failure Machine Learning Reproducibility of Results Retrospective Studies
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Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.

BMC pulmonary medicine Aug 8, 2022
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...
Airway Extubation Noninvasive Ventilation Oxygen Respiratory Insufficiency Reproducibility of Results Respiration, Artificial Machine Learning Intensive Care Units Humans
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Prediction algorithm for ICU mortality and length of stay using machine learning.

Scientific reports Jul 28, 2022
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...
Intensive Care Units Area Under Curve Humans Algorithms Mortality Machine Learning Length of Stay Retrospective Studies
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Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort.

Annals of hematology Jul 15, 2022
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 (...
Hospital Mortality Unsupervised Machine Learning Anemia, Sickle Cell Critical Care Male Brazil Humans Intensive Care Units Female Phenotype Retrospective Studies
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External validation of a machine learning model to predict hemodynamic instability in intensive care unit.

Critical care (London, England) Jul 14, 2022
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...
Retrospective Studies Intensive Care Units Humans Hemodynamics Machine Learning ROC Curve
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